Generalizedextended Weibull Power Series Family of Distributions

Size: px
Start display at page:

Download "Generalizedextended Weibull Power Series Family of Distributions"

Transcription

1 Arca Rvw o Mathatcs ad Statstcs Dcbr 205 Vol. 3 No. 2 pp SSN: (Prt (Ol Copyrght Th Author(s. All Rghts Rsrvd. Publshd by Arca Rsarch sttut or Polcy Dvlopt DO: /ars.v32a8 URL: Gralzdxtdd Wbull Powr Srs Faly o Dstrbutos Sad H. Alkar Abstract ths study w troduc a w alyo odls or lt data calld gralzd xtdd Wbullpowr srs aly o dstrbutos by copoudggralzdxtdd Wbull dstrbutos ad powr srs dstrbutos. Th copoudg procdur ollows th sa stup carrd out by Adads (998. Th proposd aly cotas all typs o cobatos btw trucatd dscrt wth gralzd ad ogralzd Wbull dstrbutos. So xstg powr srs ad subclasss o xd lt dstrbutos bco spcal cass o th proposd aly such as th copoud class o xtdd Wbull powr srsdstrbutos proposd by Slva t al. (203 ad th gralzd xpotal powr srs dstrbutostroducd by Mahoud ad Jaar (202.So athatcal proprts o th w class ar studd cludgth cuulatv dstrbuto ucto dsty ucto survval ucto ad hazard rat ucto. Th thod o axu lklhood s usd or obtag a gral stup or statg th paratrs o ay dstrbuto ths class. A xpctato-axzato algorth s troducd or statg axu lklhood stats.spcal subclasss ad applcatos or so odls aral datastar troducd to dostrat th lxblty ad th bt o ths w aly. Kyword: Gralzd xtdd Wbull powr srs dstrbutos Wbull powr srs dstrbuto gralzd powr srs dstrbutos. troducto Th odlg ad aalyss o lts s a portat aspct o statstcal work a wd varty o sctc ad tchologcal lds such as publc halth actuaral scc bodcal studs dography ad dustral rlablty. rsk odlg th lt assocatd wth a partcular rsk s ot obsrvabl asoly th axu or th u lt valu aog all th rsks ca b obsrvd. rlablty w obsrv oly th axu copot lt o a paralll syst ad th caus o alur. Lt data odlg s troducd by copoudg ay cotuous dstrbuto ad powr srs dstrbutos. Th Wbull dstrbuto s xhaustvly usd or dscrbg hazard rats du to ts gatvly ad postvly skwd dsty shaps. Chahkad ad Gajal (2009proposd th xpotal powr srs aly o dstrbutosthatgralzto a two-paratr xpotal powr srscalld th Wbull powr srs (WPS class o dstrbutos by Moras ad Barrto-Souza (20. Th WPSdstrbutos ca hav a crasg dcrasg ad upsd-dow bathtub alur rat ucto. th sa ar th xpotatd Wbull powr srs dstrbuto ad tsapplcatos wr prstd by Mahoud ad Shra (202. Rctly th xpotatdwbull Posso dstrbuto ad ts applcatos wrtroducd by Mahoud ad Spahdar (203. Th gralzd xpotal powr srs (GEPS dstrbutos wr proposd by Mahoud ad Jaar (202ollowg th sa approach dvlopd by Moras ad Barrto-Souza (20 by copoudg th gralzd xpotal ad th powr srs dstrbutos. Lahu t al. (203 proposd th powr srs dstrbutos th lt as th axu or u o th sapl wth a powr srs dstrbutd sz. Dpartt o Quattatv Aalyss Kg Saud Uvrsty Ryadh Saud Araba salkar@ksu.du.sa

2 54 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 Th copltary xpotal powr srs dstrbuto wth crasg alur rat was troducd by Jos t al. (203 as a copltto th xpotal powr srs odl proposd by Chahkady ad Gajal (2009. Slva t al. (203troducd thpowr srs dstrbutos o th copoud class o th xtdd Wbull dstrbuto. Rctly Bourgugo t al. (204 proposd a w class o atgu l dstrbuto as th Brbau Saudrs powr srs class o dstrbutos. W cob th GEPS dstrbutos troducd bymahoud ad Jaar (202ad th copoud class o th xtdd Wbull powr srs dstrbutos (EWPS proposd by Slva t al. (203 to a or gral aly calld th gralzd xtdd Wbullpowr srs (GEWPS. Cosdr a syst wth N copots whr N (th ubr o copots s a dscrt rado varabl wth support { 2...}. Th lt o th th ( 2... N copot s th ogatv cotuous rado varabl say thdstrbuto o whch blogs to o o th lt dstrbutos such as xpotal gaa Wbull ad Parto aog othrs. Th dscrt rado varabl N ca hav svraldstrbutos such as zrotrucatd Possogotrc boal logarthc ad th powr srs dstrbutos gral. Th o-gatv rado varabl dotg th lt o such a syst s dd by { } { } or basd o whthr th copots ar srs or paralll.by takg a syst wth paralll copots whch th rado varabl N has th powr srs dstrbutos ad th rado varabl ollows th gralzd Wbulldstrbuto w troduc th GEWPS class o dstrbutos that cota th GEPSad th EWPS dstrbutos as spcal cass. Ths study as to gralz th EWPS dstrbutos to obta a w ad or lxbl aly to dscrb rlablty data. Th proposd aly ca b appld to othr lds cludg busss vrot actuaral scc bodcal studs dography ad dustral rlabltyad ay othr lds. Ths aly cotas svral subclasss ad lt odls as spcal cass. Ths papr s orgazd as ollows. Scto 2 w d th class o Wbull ad gralzd Wbull dstrbutos ad dostrat th ay xstg odls that ca b dducd as spcal cass o th proposd ud odl. Scto 3 w d th GEWPS class o dstrbutos trs o dstrbuto uctos ad spcal cass o so xstg classs. Scto 4 w provd th gral proprts o th GEWPS class cludg th dsts ad thsurvval ad hazard rat uctos. Quatls ots ad ordr statstcs o GEWPS ar dscussd Scto 5. Th stato o th GEWPS paratrs s vstgatd Scto 6 usg th axu lklhood thod wthxpctato-axzato(em algorth ad a larg sapl rc. Scto 7 spcal subclasss ad so spcal dstrbutos ar troducd alog wth th lxbl athatcal ors o thrproprts. Scto 8 two odls ar prstd ad appld to llustrat how to us th proposd aly.fally so cocludg rarks ar addrssd Scto Th class o Wbull ad Gralzd Wbull dstrbuto Wbull dstrbuto s o o th ost wdly usd lt dstrbutos trs o rlablty. A larg ubr o odcatos hav b suggstd or th Wbull dstrbutoto prov th shap o th hazard rat ucto. Pgad Ya (204 prstd ay rrcs o ths attr. Th class o xtdd Wbull dstrbutos (EW was proposd by Gurvch t al. (997. Ths class s llustratd by th ollowg dto. Dto : Arado varabl dstrbuto ucto (cd s gv by G x G x x - H ( x ; W ( ; W ( - ; 0 s a br o th Wbull class o dstrbuto ts cuulatv whr H( x ; H(x s a o-gatv ootocally crasg ucto that dpds o th paratr vctor 0. Th corrspodg probablty dsty ucto (pd bcos g x g x h x x (2 H ( x W ( ; W ( ( ; 0

3 Sad H. Alkar 55 Whr h(x h( x ; s th rst drvatv o H( x.th dstrbutos o ost Wbull typsca b rwrtt or dpdg o th choc o th ucto H( x. Slva t al. (203 ad Gurvch t al. (997lstd so xapls or ths class. By usg th da o Gupta ad Cudu (999 th gralzd xpotal o ths class ca b odd as ollows: Dto 2: Arado varabl gv by - H (x G ( x ; G ( x (- ; x 0 (3 blogs to th gralzd xtdd Wbull dstrbuto class ts cd s whr H( x s a o-gatv ootocally crasg ucto thatdpds o a paratrvctor. Th corrspodg pd bcos H ( x - H ( x g ( x ; g ( x h ( x (- ; x 0 (4 whr h( x s th rst drvatv o H( x G ( x ( G (.O ca s that W x ad thus g ( x ( GW ( x gw ( x.th dstrbutos o ost Wbull ad xpotatd Wbull typsca b wrtt or (3 dpdg o th choc o th ucto H( x ad. Tabl dsplays usul H ( x ad corrspodg paratr vctorsor so xstg dstrbutos. Tabl : Spcal dstrbutos ad th corrspodg H( x ; ad vctor. Dstrbuto H ( x Rrcs Expotal x - Johso t al.(994 ( x Expotal powr [ ] Sth ad Ba (975 Burr ( x 0 log( c x c Rodrguz (977 Wbull ( x 0 x Johso t al.(994 x Modd Wbull x ( x / [ ] La t al. (2003Wbull xtso [ ] [ ] t al. (2002 Expotal powr xp[( x ] [ ] Sth ad Ba (975Parto ( x k log( x / k kjohso t al.(994 Gralzd xpotal x - Gupta ad Kudu (2000 ExpotatdWbull x Nassar ad Essa (2003 ( x / Exp. Mod. Wbull xt. [ ] [ ] Sarha ad Apaloo (203 Expotatd Raylgh x 2 - Surls ad Padgtt ( Th GEWPS aly ths scto w drv th aly o GEWPS dstrbutos by copoudg th gralzd xtdd Wbull class ad powr srs dstrbutos. Lt N b a zro trucatd dscrt rado varabl havg a powr srs dstrbuto wth th ollowg probablty ass ucto:

4 56 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 a p p( N 2... c( (5 0 whr a c ( a dpds oly o ad (0 s s chos such a way that c( s t.gv N lt N b dpdt ad dtcally dstrbutd (d rado varabls ollowg (3. Lt ( ax{ } N ( N.Thth cd o s gv by G - H ( x (x [- ] x 0. ( N ( N That s has agralzd xtddwbull class o dstrbuto wth paratrs ad basd o th sa ucto H( x. Th gralzdpowr srs dstrbutodotd by GEWPSwth acrasg ( alur rat s dd by th argal dstrbuto (cd o : a c F x ( c( c( - H ( x - H ( x ( (- ( (- x 0 whch ca b wrtt as c( G ( x F ( x p ( ( x 0. ( G x c( Not that H( x x th odl s rducd to th GEPS troducd by Mahoud ad Jaar (202. { } N Rarks.Lt th F Th cdo s c( G ( x c( ( G ( x ( x. c( c( Not that th th cd o F s: - H ( x c( ( GW ( x c( ( x c( c( whch s calld th EWPS proposd byslva t al. (203. ad H ( x F x x c( ( x c( th th cd o (6 (7 bcos Whch s th xpotal powr srs dstrbutos dvlopd by Chahkadad Gajal (2009 thatclud th lt dstrbutos class proposd by Adads ad Lukas (998 Kus (2007Tahasb ad Rza (2008.

5 Sad H. Alkar 57 (2 Lt Y G ( G ( whrg s th vrs ucto o G th Y has a GEWPS dstrbuto as F y P Y y P G G y P G G y Y ( ( ( ( ( ( ( ( c( G ( y c( F ( G ( G ( y. a ( (x ad Basd o th choc o c H wth or (6 ths class covrs th tr copoud trucatd dscrt dstrbutoswth all o thcotuous ltdstrbutos th ltratur. 4. Dsty survval ad hazard uctos Th probablty dsty uctos assocatd wth (6 ad (7 rspctvly ar gv by ad ( c ( x g(x ( G(x c( c ( (- c ( ' - H ( x H ( x - H ( x = h( x (- (8 c ( x g(x ' ( ( G ( x c ( c ( ( (- c ( ' - H ( x H ( x - H ( x = h( x (-. (9 Th survval uctos ar gv by s ( Ad s - H ( x c( G ( x c ( (- ( x (0 c( c( - H ( x c( ( G ( x c( ( (- ( x. ( c( c( Th corrspodg hazard rat uctos ar (x c ( G ( x s ( x c ( c ( G ( x ( ( x g ( x ( ( ad - H ( x H ( x - H ( x c ( (- = h ( x (- (2 - H ( x ' (x ( c ( ( G ( x ( x g(x s ( x c ( ( G ( x c ( c ( (- - H ( x H ( x - H ( x c ( ( ( - = h ( x ( -. (3 - H ( x c ( ( ( -

6 58 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 Th ltg dstrbuto o th GPS wh c( G ( x l F ( x l l c ( ( c c - H ( x c ( ( (- s a ( G ( x c c ac ( G ( x a ( G ( x c l 0 c a a G x c a whr c { a 0} Th dsts o GEWPS ca b xprssd as a t ubr o lar cobatos o dsts o th c ( a ordr statstcs. Gv that thror ' c ( G (x ( x g(x (N g ( ; ( p Y x ( c( g Y ( x ; Y ( whr s th dsty ucto o ( ax( Y... Y gv by g Y ( x ; g ( x ( G ( x h( x ; (- ( Morovr H ( x - H ( x ' c ( G (x ( x g(x p(n g ( ; Y x c( g Y ( x ; Y whr s th dsty ucto o ( Y... Y. gv by g ( x ; g ( x ( G ( x h( x (- 5. Quatls ots ad ordr statstcs ( H ( x - H ( x Lt Q b a rado varabl wth cd as (6. Th quatl ucto.. F ( (p pp (0 Q ( (. Thror c ((p c( Q ( p H og (. ( p s dd by

7 Sad H. Alkar 59 wth th cd as (7 c (( p c( Q ( p H og whr 0 p (0. Th ot gratg ucto s obtad as ollows: tx tx ( ( ( 0 0 M ( t ( x dx P ( N g ( x dx tx k P N g ( x dx P N E ( 0 ( ( ( (Y whch ca b obtad th ucto H( x. Slarly tx tx k 0 0 M ( t ( x dx P ( N g ( x dx P ( N E (Y. Ordr statstcs saog th ost udatal tools o-paratrc statstcs ad rc.t trs statoprobls ad hypothss tsts ay ways.th probablty dstrbuto ucto o th th ordr statstcs ro a rado sapl... wthdsty ucto (8 s gv by c( G ( x c( G ( x ( : ( x ( x x ( ( c( c( Usg th boal xpaso th abov orula ca b wrtt as ollows: ( : j! j c( G ( x j 0 j ( x ( x ( x 0. ( (!(! c( For th dsty ucto (9 c( G ( x c( G ( x : ( x ( x x ( c( c( Ths xprsso ca b wrtt as : 6. Estato ad rc j! j c( G ( x ( x ( x ( x 0. ( (!(! j 0 j c( Lt... x b a rado sapl wth th obsrvd valus... x obtad ro th GEWPS wth paratrs ad. Lt ( b th p paratr vctor. Th log lklhood ucto s gv by

8 60 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 H ( x l [log log log log(c( ] H ( x ( log( H ( x log(c ( ( ( ; Cosdr p H x. Th th scor ucto s gv by U ( ( l/ l/ l/ l/ T. l p c( p c( c( p c( l ( ( ( ( p H ( x ( p p c( p H x h x p c( p l p log( p ( c( p log( l k p c( p logh( x H ( x p c( p [ ( ( p p ]. p c( p k k Th axu lklhood stato (MLE o say s obtad by solvg th olar syst U (x; 0. Ths olar syst o quatos dos ot hav a closd or. For th trval stato ad hypothss tsts o th odl paratrs w rqur th ollowg obsrvd orato atrx: T T T ( ar th scod partal drvatvs o U (. Udr th stadard rgular codtos or th larg sapl approxato (Cox ad Hkly 974 ullld or th proposd odl th dstrbuto o N ( ( s approxatly p J J ( E[ ( ]. whr Whvr th paratrs ar th whr th lts o tror o th paratr spac but ot o th boudary th asyptotc dstrbutoo ( s N p (0 J ( J ( l ( whr s th ut orato atrx ad p s th ubr o paratrs N p ( ( o th dstrbuto. Th asyptotc ultvarat oral dstrbuto o ca b usd to approxat th codc trval or th paratrs th hazard rat ad th survval uctos. A 00( asyptotc codc trval or paratr s gv by ( Z Z 2 2

9 Sad H. Alkar 6 Whr s th ( dagoal lt o stadard oral dstrbuto. 6. EM algorth ( or... Z p ad 2 s th quatl / 2 o th Basd o th udrl dstrbuto th MLE o th paratrs ca b oud aalytcally usg them algorth. Th Nwto Raphso algorth s o o th stadard thods to dtr th MLEs o th paratrs. To us th algorth th scod drvatvs o th log-lklhood ar rqurd or all tratos. Th EM algorth s a vry powrul tool orhadlg th coplt data probl (Dpstr t al.977; McLachla ad Krsha 983. t s a tratv thod thatrpatdly rplacs ssg data wth statd valus ad updatsth paratr stats. t s spcally usul th coplt datast s asy to aalyz. As potd out by Lttl ad Rub (983 th EM algorth covrg rlably but rathr slowly copard wthth Nwto Raphso thod wh th aout o orato th ssg data s rlatvly larg. Rctly EM algorth has b usd by such rsarchrs asadads ad Loukas (998 Adads (999 Ng t al. (2002 Karls (2003 ad Adads t al. (2005. statg th EM algorth s a rcurrt thod whch ach stp cossts o a stat o th xpctd valu o a hypothtcal rado varabl ad latr axzs th log-lklhood o th coplt data. Lt th coplt data b wth th obsrvd valus x x ad th hypothtcal rado varabl N N. Th jot probablty ucto s such that th argal dsty o s th lklhood o trst. Th w T ( d a hypothtcal copltdata dstrbuto or ach N wth a jot probablty ucto th or o z a ( - ( N xz; z H x H x z z h( x (- c( whr x R ad z N. Thror t s straghtorward to vry that th E-stp o a EM cycl ( r Z ; ( r ( r ( r ( r ( r rqurs th coputato o th codtoal xpctato o whr ( s th currt stat ( th rth trato o Θ. Th th EM cycl s copltd wth th M-stp whch s coplt data axu lklhood ovr E Z ; wth th ssg Z s rplacd by thr codtoal xpctatos (Adads ad Loukas998 whr p Z z N x z ; a z z ( - ' - H ( x ( x c ( ( - z c( c( z az ad sc z E Z z - H ( x z 2 a z (- ts xpctd valu s z 2 - H ( x z z ' - H ( x ' - H ( x z c ( (- c ( (- z ' - H ( x - H ( x - H ( x c ( (- (- c (- ' - H ( x c ( (- ( - c (-. ' - H ( x c ( (- - H ( x - H ( x z a [ (- ] 2 - H ( x z z

10 62 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr Spcal subclasss ths scto w prst our spcal subclasss o th GEWPS aly o dstrbutos. W provd th ors o th cuulatv dsty survval ad hazard rat uctos or ad (. 6. A copoud class o th Posso ad lt dstrbutos Th copoud class o th Posso dstrbuto (CP (Alkar ad Oraby 202 s a subclass o th GEWPS ( a c ( 0 aly o dstrbuto wth. W assu that... N ar dtcally dpdt rado varabls wth a dstrbuto ucto as (3 ad wth N ollowga trucatd Posso dstrbuto at zro. Tabl 2 shows th cssary uctos or ths class. Tabl 2: Cd pd survval ad hazard rat uctos or th CL class (... N ( G(x F ( x g ( x ( x G(x s ( x g ( x ( x G(x 7.2 A copoud class o logarthc ad lts dstrbutos G(x G(x ax(... N F ( x g ( x ( x ( G(x s( x g ( x ( x (G(x (G(x Th copoud class o logarthc dstrbuto (CL (Alkar 202 s a subclass o th GEWPS aly o a ( log( (0 dstrbuto wth c. W assu that... N ar dtcally dpdt rado varabls wth a dstrbuto ucto as (3 ad wth N ollowga trucatd logarthc dstrbuto at zro. Tabl 3 shows th cssary uctos or ths class. ( G(x ( G(x Tabl 3: Cd pd survval ad hazard rat uctos or th CL class (... N ( log( ( G ( x F ( x log( g ( x ( x ( ( G(x log( log( ( G ( x s ( x log( g ( x ( x log( ( G(x[ ( G 7.3 A copoud class o gotrc ad lt dstrbutos ax(... N log( G ( x F ( x log( g ( x ( x ( G(x log( log( G ( x s ( x log( g ( x ( x ( G(x[log( G(x log Th copoud class o gotrc dstrbuto (CG (Alkar 203 s a subclass o th GEWPS aly o a ( ( dstrbuto wth c (0.

11 Sad H. Alkar 63 W assu that... N ar dtcally dpdt rado varabls wth adstrbuto ucto as (3 ad wth N ollowga trucatd gotrc dstrbuto at zro. Tabl 4 showsth cssary uctos or ths class. Tabl 4: Cd pd survval ad hazard rat uctos or th CG class N (... ( ( ( G(x F x ( G ( x ( g ( x ( x 2 ( ( G ( x ( ( G(x s ( x ( G ( x g ( x ( x ( ( G ( x ( G( 7.4 A copoud class o boal ad lt dstrbutos ( N ax(... ( G(x F ( x G ( x ( g ( x ( x 2 ( G ( x ( G(x s ( x G ( x ( g ( x ( x ( G ( x ( G(x Th copoud class o boal dstrbuto (CB (Alkar 203 s a subclass o th GEWPS aly o dstrbutos wth ( ( c. W assu that... N ar dtcally dpdt rado varabls wth a dstrbutoucto as (3 ad wth N ollowg a trucatd boal dstrbuto at zro. Tabl 5 shows th cssary uctos or ths class. Tabl 5: Cd pd survval ad hazard rat uctos or th CB class (... N ( ( ( G ( x F ( x ( g(x( ( G ( x ( x ( ( ( G ( x s ( x ( g(x( ( G ( x ( x ( ( G ( x Tabl 6 llustrats xapls o so xstg dstrbutos wth b obtad drctly ro th prvous tabls. ax(... N ( G ( x F ( x ( g(x( G ( x ( x ( ( G ( x s ( x ( g(x( G ( x ( x ( ( G ( x H ( x ad c(. Th othr uctos ca

12 64 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 Tabl 6: Spcal dstrbutos wth cd ad th corrspodg H ( x ad c( Dstrbuto H ( x a c( F ( x ( Rrcs x ( ( x Gralzd gotrc xpotal x (( Mahoud ad Jaar (202ExpotatdWbull- ( x ( ( gotrc ( x ( x (( Mahoud ad Shra(202 x ( Gralzd Posso xpotal x Mahoud ad Jaar (202 ExpotatdWbull Posso ( x Gralzd boal xpotal Gralzd logarthc xpotal ( x ( Mahoud ad Spahdar (203 x ( ( x ( ( Mahoud ad Jaar (202 x log( ( x log( log( Mahoud ad Jaar (202 ( x log( ( x log( ( ExpotatdWbull-logarthc log( Mahoud ad Spahdar (204 ( k / x Parto Posso log( x / k ; x k Slva t al. (203 Posso-Loax ( x ( x Al-Zahra ad Sagor ( Subodls ad applcatos ths scto two odls ar dscussd wth ral data as xapls o th GEWPS aly. Gotrc xpotal dstrbuto (GE ad gralzd xpotalgotrc (GEG dstrbuto ar ttd or ral data.by ( ax{ } N substtutgdrctly th ors oud Tabl 4 ro Scto 6 or w obta th ollowg pds ad hazard uctos: GE GE GEG x ( ( x ; x 2 [ ( ] ( ( x ; x ( x x ( ( ( x ; x 2 [ ( ] x x ( ( ( x ;. ( ( ( ( GEG x x

13 Sad H. Alkar 65 Fgs. ad 2 show th dsts ad hazard uctos o th GEad GEGdstrbutos or th slctd paratr valus. Fg.. Plots o th dsty ad hazard rat ucto o th GE or drt valus o ad. Fg. 2. Plots o th dsty ad hazard rat ucto o th GEG or ad drt valus ad.

14 66 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 Both odls ar ttd or th data troducd by Brbau ad Saudrs (969 o th atgu l o 606- T6 aluu coupos cut paralll wth th drcto o rollg ad oscllatd at 8 cycls pr scod. Th data arlstd Tabl 3 whch cossts o 0 obsrvatos. Tabl 7: Fatgu l o 606-T6 aluu coupos Th EMalgorth s usd to stat th odl paratrs. Th MLEs o th paratrs th axzd log lklhood th Kologorov Srov statstcs wth ts rspctv p-valu th Akak orato Crtro (AC ad Baysa orato Crtro (BC or th GE ad GEG odls ar gv Tabl 8. Th ttd dsts ad th prcal dstrbuto vrsus th ttd cds o th GE ad GEG odls oths data ar show Fg. 4. Thy dcat that th GEG dstrbuto ts th data bttr tha th GE dstrbuto. Th KS tst statstc taks th sallst valu wth th largst valu o ts corrspodg p-valu or th GEG dstrbuto. Morovr ths cocluso s cord ro th AC ad BC valus or th ttd odls gv Tabl 8.Thr dsts ad cuulatv dstrbutos ar plottd Fg.4. Tabl 8: Paratr stats KS statstc P-valu AC ad BC o th Brbau ad Saudrs data. Dst. MLE(std. K-S p-valu -log(l AC BC ˆ GE ˆ ˆ ˆ GEG ˆ Fg. 3: Plots o ttd GEG ad GE o th Brbau ad Saudrs data.

15 Sad H. Alkar Cocludg rarks W d a w aly o lt dstrbutos calld th GEWPS aly o dstrbutos whch gralzs th xtdd Wbull powr srs class ad th gralzd powr srs xpotal dstrbutos troducd by Slva t al. (203 admahoud ad Jaar (202 rspctvly. Th GEWPS class cotas ay lt subclasss ad dstrbutos. Varous stadard athatcal proprts wr drvd such as dsty adsurvval ad hazard uctos wr troducd lxbl ad usulors. Paratr stato usg themalgorth was coductd usg th axu lklhood thod. Fally w ttd so o th GEWPS odls to a ral datast to show th lxblty ad th bts o th proposd class. Ackowldgts Th author s gratul to th Dashp o Sctc Rsarch at Kg Saud Uvrstyas rprstd by th Rsarch Ctr o thcollg o Busss Adstratoor acally supportg ths study. Rrcs Alkar S. ad Oraby A. "A copoud class o Posso ad lt dstrbutos" J. Stat. Appl. Pro. vol. pp Alkar S. "Nw aly o logarthc lt dstrbutos" J. athatcs ad statstcs Vol. 8(4 pp Alkar S. "A copoud class o gotrc ad lts dstrbutos" Th op stat. ad prob. joural Vol. 5 pp Alkar S. "A class o trucatd boal lt dstrbutos" Op joural o stat. Vol. 3 pp Adads K. "A EM algorth or statg gatv boal paratrs" Austral Nw Zalad Statst. vol. 4 (2 pp Adads K. Dtrakopoulou T. ad Loukas S. "O a xtso o th xpotal gotrc Dstrbuto" Statst. Probab. Ltt. vol.73 pp Adads K. ad Loukas S. "A lt dstrbuto wth dcrasg alur rat" Statstcs ad Probablty Lttrs vol.39 pp Al-Zahra B. ad Sagor H. "Th Posso-loax dstrbuto" Rvsta Colobaa d Estadstca vol.37 pp Brbau Z. ad Saudrs S. " Estato or a aly o l dstrbutos wth applcatos to atgu" j. o appld prob. Vol. 6 pp Bourgugo M. Slva R. ad Cordro G. "A w class o atgu l dstrbutos" Joural o statstcalcoputato &sulato vol.84 pp Chahkad M. Gajal M O so lt dstrbutos wth dcrasg alur rat. Coputatoal Statstcs ad Data Aalyss Cox D. ad Hkly D. "Thortcal Statstcs " Chapa ad Hall Lodo 974. Statstcs ad Data Aalyss vol. 53 pp Dpstr A. Lard N. ad Rub D. "Maxu lklhood ro coplt data va th EM Algorth " J. Roy. Statst. Soc. Sr. B vol. 39 pp Flors Borgs J. Cacho P. ad Louzada G. "Th copltary xpotal powr srs dstrbuto" Brazla Statstcal Assocato Gupta R. ad Kudu D. "Gralzd xpotal dstrbuto: drt thod o statos" J. Statst. Coput. Sul. Vol. 00 pp Gurvch M. Dbdtto A. Raad S. " A w statstcal dstrbuto or charactrzg th rado strgth o brttl atrals J. Matr. Sc. Vol.32 pp Karls D. "A EM algorth or ultvarat Posso dstrbuto ad rlatd odls" J. Appl. Statst. vol. 30 pp Kus C A w lt dstrbuto. Coputatoal Statstcs ad Data Aalyss

16 68 Arca Rvw o Mathatcs ad Statstcs Vol. 3(2 Dcbr 205 Flors J. Borgs P. Cacho V. ad Louzada F. "Th copltary xpotal powr srs dstrbuto" Brazla Joural o probablty ad statstcs vol.27 pp Lahu A. Mutau B. ad Cataracuc S. "O th lt as th axu or u o th sapl wth powr srs dstrbutd sz" ROMA joral vol. 9 pp Lttl R. ad Rub D. "coplt data. : Kotz S. Johso N.L. (Eds. Ecyclopda o Statstcal Sccs" vol. 4 Wly NwYork983. Mahoud E. ad Jaar A. "Gralzd xpotal- powr srs dstrbutos" Coputatoal Statstcs ad Data Aalyss vol. 56 pp Mahoud E. ad Spahdar A. " Expotatd Wbull-Posso dstrbuto: odl proprts ad applcatos" Mathatcs ad coputrs sulato vol. 92 pp Mahoud E. Spahdar A. ad Lot A. "Expotatd Wbull-logarthc dstrbuto: odl proprts ad applcatos" prt arv: /204. Mahoud E. ad Shra M. "Expotatd Wbull powr srs dstrbutos ad ts applcatos" prt arv: /202. Mahoud E. ad Shra M. "Expotatd Wbull-gotrc dstrbuto ad ts applcatos"prt arv: /202. McLachla G. ad Krsha T. "Th EM Algorth ad Extso" Wly Nw York 997. Moras A. ad Barrto-Souza W. "A copoud class o Wbull ad powr srs dstrbutos" Coputatoal Statstcs ad Data Aalyss vol. 55 pp Nassar M. ad Essa F. "O th xpotatd Wbull dstrbuto"cou. Stat. Thory Mth Vol.32: pp Ng M. Cha P. ad Balakrsha N. "Estato o paratrs ro progrssvly csord data usg EM algorth" Coput. Statst. Data Aal. vol. 39 pp Pg. ad Ya Z. "Estato ad applcato or a w xtdd Wbull dstrbuto" Rlablty grg ad syst saty vol.2 pp Slva B. Bourgugo M. Das C. ad Cordro G. Th copoud class o xtdd Wbull powr srs dstrbutos" Coputatoal Statstcs ad Data Aalyss vol. 58 pp Surls J. ad Padgtt W. "rc or rlablty ad strss-strgth or a scald Burr Typ dstrbuto" Lt Data Aalyss vol. 7 pp Tahasb R. Rza S A two-paratr lt dstrbuto wth dcrasg alur rat. Coputatoal Statstcs ad Data Aalyss

Transmuted Exponentiated Gamma Distribution: A Generalization of the Exponentiated. Gamma Probability Distribution

Transmuted Exponentiated Gamma Distribution: A Generalization of the Exponentiated. Gamma Probability Distribution Appld Mathatcal Sccs Vol. 8 04 o. 7 97-30 HIKARI Ltd www.-hkar.co http//d.do.org/0.988/as.04.405 Trasutd Epotatd Gaa Dstrbuto A Gralzato o th Epotatd Gaa Probablty Dstrbuto Mohad A. Hussa Dpartt o Mathatcal

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

Suzan Mahmoud Mohammed Faculty of science, Helwan University Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION Joural of Rlablt ad Statstcal Studs; ISSN Prt: 974-84, Ol:9-5666 Vol. 6, Issu 3: 55-63 ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION Mohad A. Hussa Dpartt of

More information

A new mixed beta distribution and structural properties with applications

A new mixed beta distribution and structural properties with applications Sogklaakar J. Sc. Tchol. 37 (, 97-08, Ja. - F. 205 http://www.sst.psu.ac.th Orgal Artcl A w xd ta dstruto ad structural proprts wth applcatos Tpagor Isuk, Wa Bodhsuwa 2, ad Urawa Jaroratku * Dpartt of

More information

Inference on Stress-Strength Reliability for Weighted Weibull Distribution

Inference on Stress-Strength Reliability for Weighted Weibull Distribution Arca Joural of Mathatcs a Statstcs 03, 3(4: 0-6 DOI: 0.593/j.ajs.030304.06 Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Hay M. Sal Dpartt of Statstcs, Faculty of Corc, Al-Azhr Uvrsty, Egypt & Qass

More information

Extension of Two-Dimensional Discrete Random Variables Conditional Distribution

Extension of Two-Dimensional Discrete Random Variables Conditional Distribution Itratoal Busss Rsarch wwwccstorg/br Extso of Two-Dsoal Dscrt Rado Varabls Codtoal Dstrbuto Fxu Huag Dpartt of Ecoocs, Dala Uvrsty of Tchology Dala 604, Cha E-al: softwar666@63co Chg L Dpartt of Ecoocs,

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Odd Generalized Exponential Flexible Weibull Extension Distribution

Odd Generalized Exponential Flexible Weibull Extension Distribution Odd Gralzd Epotal Flbl Wbull Etso Dstrbuto Abdlfattah Mustafa Mathmatcs Dpartmt Faculty of Scc Masoura Uvrsty Masoura Egypt abdlfatah mustafa@yahoo.com Bh S. El-Dsouy Mathmatcs Dpartmt Faculty of Scc Masoura

More information

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data Itratoal Rfrd Joural of Egrg ad Scc (IRJES) ISSN (Ol) 319-183X, (Prt) 319-181 Volum, Issu 10 (Octobr 013), PP. 6-30 Tolrac Itrval for Expotatd Expotal Dstrbuto Basd o Groupd Data C. S. Kaad 1, D. T. Shr

More information

A new class of gamma distribution

A new class of gamma distribution Acta Sctaru http://wwwubr/acta ISSN prtd: 806-2563 ISSN o-l: 807-8664 Do: 04025/actasctcholv3929890 A w class of gaa dstrbuto Cícro Carlos Brto Frak os-slva * Ladro Chavs Rêgo 2 ad Wlso Rosa d Olvra Dpartato

More information

Using Nonlinear Filter for Adaptive Blind Channel Equalization

Using Nonlinear Filter for Adaptive Blind Channel Equalization HAMDRZA BAKHSH Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Us oar Ftr or Adaptv Bd Cha quazato MOHAMMAD POOYA Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Abstract: trsybo trrc

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

The Beta Inverted Exponential Distribution: Properties and Applications

The Beta Inverted Exponential Distribution: Properties and Applications Volum, Issu 5, ISSN (Ol): 394-894 Th Bta Ivrtd Epotal Dstrbuto: Proprts ad Applcatos Bhupdra Sgh Dpartmt of Statstcs, Ch. Chara Sgh Uvrsty, Mrut, Ida Emal: bhupdra.raa@gmal.com Rtu Gol Dpartmt of Statstcs,

More information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION

ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION Joural of Stattc: Advac Thor ad Applcato Volu 9 Nubr 03 Pag 9-35 ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION G. SRINIVASA RAO ad CH. RAMESH

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Bayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function

Bayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function Pur ad Appld Mathmatcs Joural 6; 5(6): 8-85 http://www.sccpublshggroup.com/j/pamj do:.648/j.pamj.656. ISSN: 36-979 (Prt); ISSN: 36-98 (Ol) Baysa Tst for ftm Prformac Idx of Alamuja Dstrbuto Udr Squard

More information

Inner Product Spaces INNER PRODUCTS

Inner Product Spaces INNER PRODUCTS MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w =

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

On the Beta Mekaham Distribution and Its Applications. Chukwu A. U., Ogunde A. A. *

On the Beta Mekaham Distribution and Its Applications. Chukwu A. U., Ogunde A. A. * Amrca Joural of Mathmatcs ad Statstcs 25, 5(3: 37-43 DOI:.5923/j.ajms.2553.5 O th Bta Mkaham Dstruto ad Its Applcatos Chukwu A. U., Ogud A. A. * Dpartmt of Statstcs, Uvrsty Of Iada, Dpartmt of Mathmatcs

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood

More information

A study of stochastic programming having some continuous random variables

A study of stochastic programming having some continuous random variables Itratoal Joural of Egrg Trds ad Tchology (IJETT) Volu 7 Nur 5 - July 06 A study of stochastc prograg havg so cotuous rado varals Mr.Hr S. Dosh, Dr.Chrag J. Trvd, Assocat Profssor, H Collg of Corc Navragura,

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

A Stochastic Approximation Iterative Least Squares Estimation Procedure Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Petru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials

Petru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials Ptru P Blaa-Rdu o vara by a obd sh basd o Brst olyoals REUCG OF VARACE BY A COMBE SCHEME BASE O BERSTE POYOMAS by Ptru P Blaa Abstrat A obd sh o th otrol varats ad whtd uor sal thods or rdu o vara s vstatd

More information

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT Iraa Joral of Mathatcal Chstry Vol No Dcbr 0 09 7 IJMC Two Tys of Gotrc Arthtc dx of V hylc Naotb S MORADI S BABARAHIM AND M GHORBANI Dartt of Mathatcs Faclty of Scc Arak Ursty Arak 856-8-89 I R Ira Dartt

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

ONLY AVAILABLE IN ELECTRONIC FORM

ONLY AVAILABLE IN ELECTRONIC FORM OPERTIONS RESERH o.287/opr.8.559c pp. c c8 -copao ONLY VILLE IN ELETRONI FORM fors 28 INFORMS Elctroc opao Optzato Mols of scrt-evt Syst yacs by Wa K (Vctor ha a L Schrub, Opratos Rsarch, o.287/opr.8.559.

More information

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr

More information

Linear Extractors for Extracting Randomness from Noisy Sources

Linear Extractors for Extracting Randomness from Noisy Sources 011 IEEE Itratoal Syposu o Iforato Thory Procdgs Lar Extractors for Extractg Radoss fro Nosy Sourcs Hogchao Zhou Elctrcal Egrg Dpartt Calfora Isttut of Tchology Pasada, CA 9115 Eal: hzhou@caltch.du Jhoshua

More information

Almost all Cayley Graphs Are Hamiltonian

Almost all Cayley Graphs Are Hamiltonian Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty

More information

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

Large N phase transitions in Supersymmetric gauge theories with massive matter

Large N phase transitions in Supersymmetric gauge theories with massive matter Lar phas trastos Suprsytrc au thors wth assv attr Mul Trz trz@uc.s Uvrsdad Copluts d Madrd Basd o: J. Russo ad K. arbo arv:309.004 3.4 30.6968 A. Barraco ad J. Russo arv:40.367 J. Russo G. Slva ad M.T.

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

A Measure of Inaccuracy between Two Fuzzy Sets

A Measure of Inaccuracy between Two Fuzzy Sets LGRN DEMY OF SENES YERNETS ND NFORMTON TEHNOLOGES Volum No 2 Sofa 20 Masur of accuracy btw Two Fuzzy Sts Rajkumar Vrma hu Dv Sharma Dpartmt of Mathmatcs Jayp sttut of formato Tchoy (Dmd vrsty) Noda (.P.)

More information

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov 199 Algothm ad Matlab Pogam fo Softwa Rlablty Gowth Modl Basd o Wbull Od Statstcs Dstbuto Akladswa Svasa Vswaatha 1 ad Saavth Rama 2 1 Mathmatcs, Saaatha Collg of Egg, Tchy, Taml Nadu, Ida Abstact I ths

More information

A Bivariate Distribution with Conditional Gamma and its Multivariate Form

A Bivariate Distribution with Conditional Gamma and its Multivariate Form Joural of Moder Appled Statstcal Methods Volue 3 Issue Artcle 9-4 A Bvarate Dstrbuto wth Codtoal Gaa ad ts Multvarate For Sue Se Old Doo Uversty, sxse@odu.edu Raja Lachhae Texas A&M Uversty, raja.lachhae@tauk.edu

More information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

The Odd Generalized Exponential Modified. Weibull Distribution

The Odd Generalized Exponential Modified. Weibull Distribution Itatoal Mathmatcal oum Vol. 6 o. 9 943-959 HIKARI td www.m-ha.com http://d.do.og/.988/m.6.6793 Th Odd Galzd Epotal Modd Wbull Dstbuto Yassm Y. Abdlall Dpatmt o Mathmatcal Statstcs Isttut o Statstcal Studs

More information

Lecture #11. A Note of Caution

Lecture #11. A Note of Caution ctur #11 OUTE uctos rvrs brakdow dal dod aalyss» currt flow (qualtatv)» morty carrr dstrbutos Radg: Chatr 6 Srg 003 EE130 ctur 11, Sld 1 ot of Cauto Tycally, juctos C dvcs ar formd by coutr-dog. Th quatos

More information

Estimation of the Present Values of Life Annuities for the Different Actuarial Models

Estimation of the Present Values of Life Annuities for the Different Actuarial Models h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld

More information

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions IOSR Joural o Elcrcal ad Elcrocs Egrg IOSR-JEEE -ISSN: 78-676,p-ISSN: 3-333, Volu, Issu 5 Vr. III Sp - Oc 6, PP 9-96 www.osrourals.org kpa s lgorh o Dr Sa Traso Marx ad Soluo o Dral Euaos wh Mxd Ial ad

More information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

More information

i j i i i = S i 1 Y Y i F i ..., X in

i j i i i = S i 1 Y Y i F i ..., X in R ASHI :43-48 (06 ESIA IO OF HE SALLES LOCA IO OF WO EGAIVE EXPOEIAL POPULAIOS Partha Pal ad Uttam Badyopadhyay Dpartmt of Statsts aulaa Azad Collg Kolkata Dpartmt of Statsts Uvrsty of Calutt a ABSRAC

More information

BAYESIAN ANALYSIS OF THE SIMPLE LINEAR REGRESSION WITH MEASUREMENT ERRORS

BAYESIAN ANALYSIS OF THE SIMPLE LINEAR REGRESSION WITH MEASUREMENT ERRORS BAYESIAN ANALYSIS OF THE SIMPLE LINEAR REGRESSION WITH MEASUREMENT ERRORS Marta Yuk BABA Frado Atoo MOALA ABSTRACT: Usually th classcal approach to mak frc lar rgrsso modl assums that th dpdt varabl dos

More information

MEANINGFUL BATTING AVERAGES IN CRICKET

MEANINGFUL BATTING AVERAGES IN CRICKET MEANINGFUL BATTING ERAGES IN CRICKET Paul J. va Stad, Albrtus T. Mrg, Joha A. Sty, Igr N. Fabrs-Rotll Dpartt of Statstcs, Uvrsty of Prtora, Prtora, 000, SOUTH AFRICA Eal: paul.vastad@up.ac.za Wb: www.up.ac.za/paulvastad

More information

T and V be the total kinetic energy and potential energy stored in the dynamic system. The Lagrangian L, can be defined by

T and V be the total kinetic energy and potential energy stored in the dynamic system. The Lagrangian L, can be defined by From MEC '05 Itrgratg Prosthtcs ad Mdc, Procdgs of th 005 MyoElctrc Cotrols/Powrd Prosthtcs Symposum, hld Frdrcto, Nw Bruswc, Caada, ugust 7-9, 005. EECROMECHNIC NYSIS OF COMPEE RM PROSHESIS (EMS) Prmary

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays Itratoal Cofrc o Appld Mathmatcs, Smulato ad Modllg (AMSM 6) Group Cossus of Scod-Ordr Mult-agt Ntworks wth Multpl Tm Dlays Laghao J* ad Xyu Zhao Chogqg Ky Laboratory of Computatoal Itllgc, Chogqg Uvrsty

More information

Priority Search Trees - Part I

Priority Search Trees - Part I .S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal

More information

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

Entropy Equation for a Control Volume

Entropy Equation for a Control Volume Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Power Spectrum Estimation of Stochastic Stationary Signals

Power Spectrum Estimation of Stochastic Stationary Signals ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:

More information

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations Appld Mathmatcal Sccs ol. 9 5 o. 43 75-73 HKAR Ltd www.m-hkar.com http://dx.do.org/.988/ams.5.567 Thr-Dmsoal Thory of Nolar-Elastc Bods Stablty udr Ft Dformatos Yu.. Dmtrko Computatoal Mathmatcs ad Mathmatcal

More information

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities.

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities. Baysia Ntworks Motivatio Th coditioal idpdc assuptio ad by aïv Bays classifirs ay s too rigid spcially for classificatio probls i which th attributs ar sowhat corrlatd. W talk today for a or flibl approach

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

New families of p-ary sequences with low correlation and large linear span

New families of p-ary sequences with low correlation and large linear span THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volu 4 Issu 4 Dcbr 7 TONG X WEN Qao-ya Nw fals of -ary sucs wth low corrlato ad larg lar sa CLC ubr TN98 Docut A Artcl ID 5-8885 (7 4-53-4

More information

Construction of Composite Indices in Presence of Outliers

Construction of Composite Indices in Presence of Outliers Costructo of Coposte dces Presece of Outlers SK Mshra Dept. of Ecoocs North-Easter Hll Uversty Shllog (da). troducto: Oftetes we requre costructg coposte dces by a lear cobato of a uber of dcator varables.

More information

Ordinary Least Squares at advanced level

Ordinary Least Squares at advanced level Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw

More information

Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform

Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform Appl. Sc. 4, 4, 99-7; do:.339/app499 Artcl OPEN ACCESS appld sccs ISSN 76-347 www.mdp.com/joural/applsc Roud-Off Nos of Multplcatv FIR Fltrs Implmtd o a FPGA Platform Ja-Jacqus Vadbussch, *, Ptr L ad Joa

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

More information

Fourier Transforms. Convolutions. Capturing what s important. Last Time. Linear Image Transformation. Invertible Transforms.

Fourier Transforms. Convolutions. Capturing what s important. Last Time. Linear Image Transformation. Invertible Transforms. orr Trasors Rq rad: Captr 7 92 &P Adso Soc ad ra (adot o) Opt rad: Hor 7 & 8 P 8 Last T Cooto trs: a/box tr Gassa tr t drc tr Lapaca o Gassa tr Ed Dtcto Cootos Cooto s coptatoay costy ad a copx oprato

More information

Numerical Analysis of Sandwiched Composite-FSS Structures

Numerical Analysis of Sandwiched Composite-FSS Structures Nurcal Aalyss of Sadwchd Copost-FSS Structurs Ru Qag ad J Ch Dpartt of lctrcal ad Coputr grg Uvrsty of Housto Housto, TX 7724 Jgyu Huag, Mara Koldtsva, Rchard Dubroff, ad Jas Drwak MC Laboratory, C Dpartt,

More information

1985 AP Calculus BC: Section I

1985 AP Calculus BC: Section I 985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

Reinforcement Learning-based Output Feedback Control of Nonlinear Systems with Input Constraints

Reinforcement Learning-based Output Feedback Control of Nonlinear Systems with Input Constraints Rfct Larg-basd Output Fdbac Cotrol of Nolar Systs wth Iput Costrats P. H ad S. Jagaatha uow IO olar dscrt syst. h Abstract A ovl ural tw (NN -basd output fdbac cotrollr wth agtud costrats s dsgd to rfct

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Optimal Progressive Group-Censoring Plans for. Weibull Distribution in Presence. of Cost Constraint

Optimal Progressive Group-Censoring Plans for. Weibull Distribution in Presence. of Cost Constraint It J Cotmp Mat Sccs Vol 7 0 o 7 337-349 Optmal Progrssv Group-Csorg Plas for Wbull Dstrbuto Prsc of Cost Costrat A F Atta Dpartmt of Matmatcal Statstcs Isttut of Statstcal Stus & Rsarc Caro Uvrsty Egypt

More information