Estimation in Step-Stress Partially Accelerated Life Tests for the Burr Type XII distribution Using Type I Censoring By. Abstract

Size: px
Start display at page:

Download "Estimation in Step-Stress Partially Accelerated Life Tests for the Burr Type XII distribution Using Type I Censoring By. Abstract"

Transcription

1 Estmto Step-Stress Prtll elerted fe Tests for the Burr Tpe XII dstrbuto Usg Tpe I Cesorg B bd-elftth,. M. _ftth@hotml.om ml S. Hss ml5_solm@hoo.om d Nssr, S. G. sdbosd@hoo.om Isttute of Sttstl Studes & Reserh Cro Uverst bstrt Some trdtol lfe tests result ver few flures b the ed of the test. So tht to obt flure qul test uts re ru t hgher th usul stress odtos. Ths stud s devoted to the estmtg flure tme dt uder step-stress prtll elerted lfe tests bsed o tpe I esorg. The lfetme dstrbuto of the test tems s ssumed to follow Burr tpe XII dstrbuto. The mxmum lelhood estmtes re obted for the dstrbuto prmeters d elerto ftor. I ddto, smptot vre d ovre mtrx of the estmtors re gve. tertve proedure s used to obt the estmtors umerll usg Mthd (). Furthermore, ofdee tervls of the estmtors re preseted. For llustrtg the preso d vrtos of mxmum lelhood estmtors smulto results re luded for dfferet smple szes. Ke words: Relblt; Step stress-prtll elerted lfe test; elerted ftor, Burr tpe XII dstrbuto; Mxmum lelhood method; Fsher formto mtrx;. Itroduto ue to otul mprovemet mufturg desg, t s more dffult to obt formto bout lfetme of produts or mterls wth hgh relblt t the tme of testg uder orml odtos. Ths mes lfetme testg uder these odtos ver ostve, te log tme. To get the formto bout the lfetme dstrbuto of these mterls, smple of these mterls s subjeted to more severe operto odtos th orml oes. These odtos re lled stresses whh m be the form of temperture, voltge, pressure, vbrto, lg rte, lod, et. Ths d of testg s lled elerted lfe test (T), where produts re put uder stresses hgher th usul to eld more flure dt short tme. The lfe dt from the hgh stresses re used to estmte the lfe dstrbuto t desg

2 odto. There re ml three T methods. The frst method s lled the ostt stress T; the seod oe s referred to s step-stress T d the thrd s the progressve stress T. the frst method s used whe the stress rems uhged, so tht f the stress s we, the test hs to lost for log tme. The other two methods, redue the testg tme d sve lot of mpower, mterl soures d moe (see Ro; (99)). The mjor ssumpto T s tht the mthemtl model reltg the lfetme of the ut d the stress s ow or be ssumed. I some ses, suh lfe-stress reltoshps re ot ow d ot be ssumed,.e T dt ot be extrpolted to use odto. So, suh ses, prtll elerted lfe tests (PT) s more sutble test to be performed for whh tested uts re subjeted to both orml d elerted odtos. ordg to Nelso (99), the stress be ppled vrous ws. Oe w to elerte flure step-stress, whh reses the stress ppled to test produt spefed dsrete sequee. Step-stress prtll elerted lfe test (SS- PT) s used to get qul formto for the lfetme of produt wth hgh relblt; spell, whe the mthemtl model relted to test odtos of me lfetme of the produt s uow d ot ssumed. The step stress sheme pples stress to test uts the w tht the stress wll be hged t prespefed tme. Geerll, test ut strts t spefed low stress. If the ut does ot fl t spefed tme, stress o t rsed d held spefed tme.. Stress s repetedl resed utl test ut fls or esorg tme s rehed. For overvew of SS-PT, there s mout of lterture o desgg SS- PT. Goel (97) osdered the estmto problem of the elerted ftor usg both mxmum lelhood d Bes methods for tems hvg expoetl d uform dstrbutos. egroot d Goel (979) estmted the prmeters of the expoetl dstrbuto d elerto ftor SS-PT usg Bes pproh, wth dfferet loss futos. lso, Bhtthr d Soejoet (989) estmted the prmeters of the Webull dstrbuto d elerto ftor usg mxmum lelhood method. B d hug (99) estmted the sle prmeter d elerto ftor for expoetl dstrbuto uder tpe I esored smple usg mxmum lelhood method. tt et l (996) osdered the mxmum lelhood method for estmtg the elerto ftor d the prmeters of Webull dstrbuto SS-PT uder tpe I esorg. bel-ghl et l (997) used Bes pproh for estmtg the prmeters of Webull dstrbuto prmeters wth ow shpe prmeter. The

3 studed the estmto problem SS-PT uder both tpe I d tpe II esored dt. bdel-gh (998) osdered the estmto problem of the prmeters of Webull dstrbuto d the elerto ftor for both SS-PT d ostt-stress PT. Mxmum lelhood d Bes methods uder tpe I d tpe II esored dt re ppled ths stud. bdel-ghl et l () studed the estmto problem of the elerto ftor d the prmeters of Webull dstrbuto SS- PT usg mxmum lelhood method two tpes of dt, mel tpe I d tpe II esorg. bdel-ghl et l (b, 3) studed both the estmto d optml desg problems for the Preto dstrbuto uder SS-PT wth tpe I d tpe II esorg. bdel-gh (4) osdered the estmto problem of log-logst dstrbuto prmeters uder SS-PT. Reetl, Isml (4) used mxmum lelhood d Bes methods for estmtg the elerto ftor d the prmeters of Preto dstrbuto of the seod d. Isml (6) studed the estmto d optml desg problems for the Gompertz dstrbuto SS-PT wth tpe I esored dt. Ths rtle s to fous o the mxmum lelhood method for estmtg the elerto ftor d the prmeters of Burr tpe XII dstrbuto. Ths wor ws oduted for SS-PT uder tpe I esored smple. The performe of the obted estmtors s vestgted terms of reltve bsolute bs, me squre error d the stdrd error. Moreover, the ofdee tervls of the estmtors wll be obted. Ths rtle be orgzed s follows. I seto the Burr tpe XII dstrbuto s trodued s lfetme model d the test method s lso desrbed. Seto 3 presets pot d tervl estmtes of prmeters d elerto ftor for the Burr tpe XII uder tpe I esorg usg mxmum lelhood method. I seto 4 the pproxmte smptot vres d ovres mtrx re vestgted. Seto 5 expls the smulto studes for llustrtg the theoretl results. Fll, olusos re luded seto 6. Tbles re dspled the ppedx.. The Model d Test Model s member of the Burr (94) fml of dstrbutos, ths ludes twelve tpes of umultve dstrbuto futos wth vret of dest shpes. The two prmeter Burr tpe XII dstrbuto deoted b Burr (, ) hs dest futo of the form 3

4 f (,, ) ( ) ( t t t ), t >, > d > where, d re the shpe prmeters of the dstrbuto. The umultve dstrbuto futo s F ( ) ( ) ( t,, t ), t >, > d > The relblt futo of the Burr tpe XII dstrbuto s gve b: (.) (.) (, ) ( t ) ( ) R t, (.3) The Burr (, ) dstrbuto ws frst proposed s lfetme model b ube (97, 973). Evs d Smos (975) studed further the dstrbuto s flure model d the lso derved the mxmum lelhood estmtors s well momets of the Burr (, ) probblt dest futo. ews (98) oted tht the Webull d expoetl dstrbutos re spel lmtg ses of the prmeter vlues of the Burr (, ) dstrbuto. She proposed the use of the Burr (, ) dstrbuto s model elerted lfe test dt. I SS-PT, ll of the uts re tested frst uder orml odto, f the ut does ot fl for prespefed tme, the t rus t elerted odto utl flure. Ths mes tht f the tem hs ot fled b some prespefed tme, the test s swthed to the hgher level of stress d t s otued utl tems fls. The effet of ths swth s to multpl the remg lfetme of the tem b the verse of the elerto ftor. I ths se the swthg to the hgher stress level wll shorte the lfe of test tem. Thus the totl lfetme of test tem, deoted b Y, psses through two stges, whh re the orml d elerted odtos. The the lfetme of the ut SS-PT s gve s follows: T f T Y (.4) ( T ) f T >, where, T s the lfetme of tem t use odto, s the stress hge tme d s the elerto ftor whh s the rto of me lfe t use odto to tht t elerted odto, usull >. ssume tht the lfetme of the test tem follows Burr tpe XII dstrbuto wth shpe prmeters d dest futo of totl lfetme Y of tem s gve b:. Therefore, the probblt 4

5 f ( ) f ( ) f ( ) f f f < > (.5) ( ) ( ) ( ) where, f,, s the equvlet form to equto (.), d, f > ( ) [ ( )] { ( )} [ ] ( ),, >, > b the trsformto vrble tehque usg equtos (.) d (.4). 3. Mxmum elhood Estmto, s obted The mxmum lelhood s oe of the most mportt d wdel methods, used sttsts. The de behd mxmum lelhood prmeter estmto s to determe the prmeters tht mxmze the probblt (lelhood) of the smple dt. Furthermore, mxmum lelhood estmtors re osstet d smptotll ormll dstrbuted. I ths seto pot d tervl estmto for the prmeters d elerto ftor of Burr tpe XII dstrbuto bsed o tpe I esorg re evluted usg mxmum lelhood method. 3. Pot Estmtes I tpe I esorg the test termtes whe the esorg tme η s rehed. The observed vlues of the totl lfetme Y re..., where u d re the umbers of () <... < ( ) < ( ) < < ( ) η u u u tems fled t orml odtos d elerted odtos respetvel. et, be dtor futos, suh tht (),,..., otherwse d, < ( j ) η,,..., otherwse For smplfg () be expressed b. Se the lfetmes,..., of tems re depedet d detll dstrbuted rdom vrbles, the ther lelhood futo s gve b 5

6 ( ) ( ) ( ) { } ( ) [ ] { } ( ) [ ] ( ) { } ( ) [ ] { },, ; η (3.) where, d. It s usull eser to mxmze the turl logrthm of the lelhood futo rther th the lelhood futo tself. Therefore, the logrthm of the lelhood futo s (3.) ( ) ( ) [ ] [ ] ( ) [ ] l l l l l l l l l where, { }] [, ( )] [ η,, u, u d u Mxmum lelhood estmtors of, d re solutos to the sstem of equtos obted b lettg the frst prtl dervtves of the totl log lelhood be zero wth respet to, d, respetvel. Therefore, the sstem of equtos s s follows: ( ) ( )( ) ( ) ( ) ( )( ) ( ) ( ) ( )( ), l η (3.3) ( )( ) ( ) ( ) ( ) ( ) ( ) l l l l l l (3.4) d[ ( ) ( ) ( ) ( l l l l ). (3.5) From equto (3.5) the mxmum lelhood estmte of s expressed b 6

7 o, (3.6) where, ( ) ( ) ( ) ( ) l l l Cosequetl, b substtutg for to equtos (3.3) d (3.4), the sstem equtos re redued to two oler equtos s follows: l l 3 (3.7) d ( ) ( ) 5 4 (3.8) where, ( ) ( ) ( ) l l,, ( )( ) ( ) 3 l ( ) ( )( ) 4 d ( ) ( ) ( )( ) 5 η. Se the losed form soluto to oler equtos (3.7) d (3.8) re ver hrd to obt. tertve proedure s ppled to solve these equtos umerll usg Mthd () sttstl pge. Newto-Rphso method s ppled for smulteousl solvg the oler equtos to obt d. Therefore s lulted esl from equto (3.6). 3. Itervl Estmtes If d ),..., ( θ θ ),..., ( U U θ θ re futo of the smple dt the ofdee tervl for populto prmeter,..., θ s gve b γ θ θ θ ] [ U p (3.9) where, d re the lower d upper ofdee lmts whh elose θ θ U θ wth probbltγ. The tervl s lled two sded ], [ θ θ U % γ ofdee tervl for θ. For lrge smple sze, the mxmum lelhood estmtes, uder pproprte regulrt odtos, re osstet d smptotll ormll dstrbuted. 7

8 Therefore, the two sded pproxmte γ % ofdee lmts for the mxmum lelhood estmte θ of populto prmeter θ be ostruted, suh tht θ θ p [ z z] γ (3.) σ ( ) θ where, ( γ ) z s the [ ] stdrd orml peretle. Therefore, the two sded pproxmte γ % ofdee lmts for populto prmeter θ be obted, suh tht p[ θ zσ ( ) θ θ θ zσ ( θ )] γ (3.) The, the two sded pproxmte ofdee lmts for, d wll be ostruted usg equto (3.) wth ofdee levels 95 % d 99 %. 4. smptot Vres d Covre s of Estmtes The smptot vres of mxmum lelhood estmtes re gve b the elemets of the verse of the Fsher formto mtrx I ( θ ) E{ l θ θ } Ufortutel, the ext mthemtl expressos for the bove expetto re ver dffult to obt. Therefore, the observed Fsher formto mtrx s gve b I ( θ ) { l θ θ } j j, whh s obted b droppg the expetto o operto E [see Cohe (965)]. The pproxmte (observed) smptot vre ovre mtrx F for the mxmum lelhood estmtes be wrtte s follows F [ ( θ )],, j,, 3 I j d ( ) (,,) j θ (4.) [ The seod prtl dervtves of the mxmum lelhood futo re gve s the followg: l ( ) ( ) ( ) [ ] ( ) ( )( )( )( ) ( ) ( )( ) ( ) [ ], (4.) ( ) ( η )( )( η )( ) ( ) ( η )( ) ( ) j. 8

9 ( )( ) ( )( )( ) ( ) ( ) ( ) ( ) ( ) ( ) [ ] ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) [ ], l l l l l η (4.3) [[ ( ) ( )( ) ( ) ( ) ( )( ), l η (4.4) [ ( ) ( ) ( ) ( ) ( ) [ ] ( ) ( ) ( )( ) { } ( ) ( ) ( ) ( ) ( ) { }, l l l l l l l l l l (4.5) ( ) ( ) ( ) ( ) ( )( ) ( ), l l l l (4.6) [[ d l. (4.7) Cosequetl, the mxmum lelhood estmtors of, d hve smptot vre ovre mtrx defed b vertg the Fsher formto mtrx d b substtutg for F, for d for. 5. Smulto Studes Smulto studes hve bee performed usg Mthd () for llustrtg the theoretl results of estmto problem. The performe of the resultg estmtors of the elerto ftor d two shpe prmeters hs bee osdered terms of ther bsolute reltve bs (RBs), me squre error (MSE), d reltve error (RE). Furthermore, the smptot vre d ovre mtrx d twosded ofdee tervls of the elerto ftor d two shpe prmeters re obted. The Smulto proedures were desrbed below: 9

10 Step : rdom smples of szes (5) 4 d 5 were geerted from Burr tpe XII dstrbuto. The geerto of Burr tpe XII dstrbuto s ver smple, fu hs uform (,) rdom umber, the Y ( ) ( ) [( U ) ] follows Burr tpe XII dstrbuto. The true prmeters seleted vlues re (,.5, ) d (.5,,.5) Step : Choosg the esorg tme t the orml odto to be 3 d esorg tme of PT to beη 6. Step 3: For eh smple d for the two sets of prmeters, the elerto ftor d the prmeters of dstrbuto were estmted SS-PT uder tpe I esored smple. Step 4. Newto Rphso method ws used for solvg the two oler lelhood for d gve equtos (3.7) d (3.8), respetvel. The estmte of the shpe prmeter ws esl obted from equto (3.6) Step 5: The RBs, MSE, d RE of the estmtors for elerto ftor d two shpe prmeters for ll smple szes d for two sets of prmeters were tbulted. Step 6: The smptot vre d ovre mtrx of the estmtors for dfferet smple szes were obted. Step 7: The two sded ofdee lmt wth ofdee level γ. 95 d γ. 99 of the elerto ftor d the two shpe prmeters were ostruted. Smulto results re summrzed Tbles, d 3. Tble gves the RBs, MSE, d RE of the estmtors. The smptot vres d ovre mtrx of the estmtors re dspled Tble. Whle Tble 3 presets the pproxmted two-sded ofdee lmts t 95 % d 99% for the prmeters d elerto ftor. From these tbles, the followg observtos be mde o the performe of SS-PT prmeter estmto of Burr tpe XII lfetme dstrbuto:. For the frst set of prmeters (.5,,.5), the mxmum lelhood estmtors hve good sttstl propertes th the seod set of prmeters (,.5, ) for ll smple szes (see Tble).. s the elerto ftor reses the estmtes hve smller MSE, d RE. s the smple sze reses the RBses d MSEs of the estmted prmeters derese. Ths dtes tht the mxmum lelhood estmtes provde

11 smptotll ormll dstrbuted d osstet estmtors for the prmeters d elerto ftor 3. The smptot vres of the estmtors re deresg whe the smple sze resg (see Tble). 4. The tervl of the estmtors dereses whe the smple sze s resg. lso, the tervl of the estmtor t γ. 95 s smller th the tervl of estmtor t γ.99 (see Tble3). 6. Coluso For produts hvg hgh relblt, the test of produt lfe uder orml use ofte requres log perod of tme. So T or PT s used to fltte estmtg the relblt of the ut short perod of tme. I T test tems re ru ol t elerted odtos, whle PT the re ru t both orml d elerted odtos. Oe w to elerte flure s the SS- PT whh reses the stresses ppled to test produt spefed dsrete sequee. The lfetme of the test tems s ssumed to follow the Burr tpe XII dstrbuto. Uder tpe I esorg, the test ut s frst ru t orml use odto, d f t does ot fl for spefed tme, the t s ru t elerted odto utl esorg tmeη s rehed. The mxmum lelhood method s used for estmtg the elerto ftor d prmeters of Burr tpe XII dstrbuto uder tpe I esorg. Performe of step-stress testg pls d model ssumptos re usull evluted b the propertes of the mxmum lelhood estmtes of model prmeters. I ths stud, the frst set of prmeters hve good sttstl propertes th the seod set of prmeters for ll smple szes Mxmum lelhood estmtors re osstet d smptotll ormll dstrbuted. s the smple sze reses the smptot vre d ovre of estmtors dereses. Regrdg the tervl of estmtors, t be oted tht the tervl of the estmtors t γ. 99 s greter th the orrespodg t γ. 95. lso, s the smple sze reses the tervl of the estmtors dereses for the two ofdee level.

12 ppedx Tble: The RBs, MSE d RE of the Prmeters (,,,, η) Uder Tpe I Cesorg Prmeters (,.5,,3,6) (.5,,.5,3,6) (,,,, η) RBs MSE RE RBs MSE RE

13 Tble: smptot Vres d Covres of Estmtes Uder Tpe Cesorg (,.5,,3,6) (.5,,.5,3,6) ĉ ĉ

14 Tble3: Cofdee Bouds of the Estmtes t Cofdee level t.95 d.99 Prmeters (,.5,,3,6) (.5,,.5,3,6) (,,,, η) Stdrd ower Upper Stdrd ower Upper Boud devto Boud Boud devto Boud

15 Referees. bdel-ghl,.., tt,. F. d bdel-gh, M. M. (997). "The Bes Estmto of Webull Prmeters Step Prtll elerted lfe Tests wth Cesored t". Proeedg of the 3 st ul Coferee of Sttsts, Computer Sees d Operto Rreserh, ISSR, Cro Uverst, bdel-ghl,.., tt,. F. d bdel-gh, M. M. (). "The Mxmum elhood Estmtes Step Prtll elerted lfe Tests for the Webull Prmeters Cesored t". Commto Sttsts-Theor d Methods, 3(4), bdel-ghl,.., El-Khodr, E. H. d Isml,.., (b). "Mxmum elhood Estmto d Optml esg Step-Stress Prtll elerted lfe Tests for the Preto dstrbuto wth Tpe I Cesorg". Proeedg of the 4 st ul Coferee o Sttsts d Computer Modelg Hum d Sol Sees, Fult of Eooms d Poltl See, Cro Uverst, bdel-ghl,.., El-Khodr, E. H. d Isml,.., (3). "Estmto d Optml esg Step Prtll elerted lfe Tests for the Compoud Preto dstrbuto usg Tpe II Cesorg". Proeedg of the 5 st ul Coferee o Sttsts d Computer Modelg Hum d Sol Sees, Fult of Eooms d Poltl See, Cro Uverst, bdel-gh, M. M., (998). "Ivestgtos of some lfetme Models uder Prtll elerted lfe Tests". Ph.. Thess, eprtmet of Sttsts, Fult of Eooms d Poltl See, Cro Uverst, Egpt. 6. bdel-gh, M. M., (4). "The Estmto Problem of the og ogst Prmeters Step Prtll elerted fe Tests usg Tpe I Cesored t". The Ntol Revew of Sol Sees, 4 (), tt,. F., bel-ghl,.., d bel-gh-, M. M. (996). "The Estmto Problem of Prtll elerted fe Tests for Webull dstrbuto b Mxmum lelhood Method wth Cesored t". Proeedg of the 3 st ul Coferee of Sttsts, Computer Sees d Operto Reserh, ISSR, Cro Uverst, B,.S., Chug, S.W. (99). "Optml esg of Prtll elerted fe Tests for the Expoetl strbuto uder Tpe I Cesorg". IEEE Trs. Relblt 4,

16 9. Bhtthr, G. K. Soejoet, Z. (989). " Tmpered Flure Rte Model for Step-Stress elerted fe Test". Commuto Sttsts-Theor d Methods, 8, Burr, H. W. (94). "Cumultve Freque Futos".. Mth. Sttst. 3, Cohe,. C. (965). "Mxmum elhood Estmto the Webull strbuto Bsed o Complete d o Cesored Smples". Tehometrs, 5, egroot, M. H. d Goel, P. K. (979). "Bes estmto d optml esg Prtll elerted lfe Testg". Nvl Reserh ogsts Qurterl, 6() ube, S.. (97). "Sttstl Cotrbutos to Relblt Egeerg". R TR 7-, ube, S.. (973) "Sttstl Tretmet of Cert fe Testg d Relblt Problems". R TR 73-55, Evs, R.. d Smos, G. (975). "Reserh o Sttstl Proedure Relblt Egeerg". R-TR-75-54, Goel, P. K. (97). "Some Estmto problems the stud of Tmpered Rdom Vrbles". Ph.. Thess eprtmet of Sttsts, Crege-Mello Uverst, Pttspurgh, Peslv. 7. Isml,.. (4), "The Test esg d Prmeter Estmto of Preto fetme strbuto uder Prtll elerted fe Tests". Ph.. eprtmet of Sttsts, Fult of Eooms d Poltl See, Cro Uverst, Egpt. 8. Isml,.. (6). "O the Optml esg of Step-Stress Prtll elerted fe Tests for the Gompertz strbuto wth Tpe I Cesorg". IterStt, Eltro Jourl ews,. W. (98), "The Burr strbuto s Geerl Prmetr Fml Survvorshp d Relblt ppltos". Ph.. ep. Of Bostst. Uv. North. Crol.. Nelso. W. (99), " elerted fe Testg: Sttstl Models, Test Pls d t lss". Joh, Wle d Sos, New or.. Ro, B.R. (99), " Equvlee of the Tmpered Rdom Vrbles d Tmpered Flure Rte Models T for Clss of fe strbuto Hvg the Settg the Clo B to zero Propert." Commuto Sttsts-Theor d Methods, (3),

On Testing Simple and Composite Goodness-of-Fit Hypotheses When Data are Censored

On Testing Simple and Composite Goodness-of-Fit Hypotheses When Data are Censored ALT`8 Jue 9- Bordeux O Testg mple d Composte Goodess-of-Ft Hypotheses Whe Dt re Cesored 35 EV Chmtov BYu Lemesho Novosbrs tte Tehl Uversty Russ Abstrt Problems of pplto of the oprmetr olmogorov Crmer-vo

More information

Chapter Gauss-Seidel Method

Chapter Gauss-Seidel Method Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos

More information

Numerical Analysis Topic 4: Least Squares Curve Fitting

Numerical Analysis Topic 4: Least Squares Curve Fitting Numerl Alss Top 4: Lest Squres Curve Fttg Red Chpter 7 of the tetook Alss_Numerk Motvto Gve set of epermetl dt: 3 5. 5.9 6.3 The reltoshp etwee d m ot e ler. Fd futo f tht est ft the dt 3 Alss_Numerk Motvto

More information

Implicit Runge-Kutta method for Van der pol problem

Implicit Runge-Kutta method for Van der pol problem Appled d Computtol Mthemts 5; 4(-: - Publshed ole Jul, 4 (http://www.seepublshggroup.om//m do:.48/.m.s.54. ISSN: 8-55 (Prt; ISSN: 8-5 (Ole Implt Ruge-Kutt method for V der pol problem Jfr Bzr *, Mesm Nvd

More information

Chapter 7. Bounds for weighted sums of Random Variables

Chapter 7. Bounds for weighted sums of Random Variables Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

Diagonally Implicit Runge-Kutta Nystrom General Method Order Five for Solving Second Order IVPs

Diagonally Implicit Runge-Kutta Nystrom General Method Order Five for Solving Second Order IVPs WSEAS TRANSACTIONS o MATHEMATICS Fudzh Isml Dgoll Implt Ruge-Kutt Nstrom Geerl Method Order Fve for Solvg Seod Order IVPs FUDZIAH ISMAIL Deprtmet of Mthemts Uverst Putr Mls Serdg Selgor MALAYSIA fudzh@mth.upm.edu.m

More information

Islamic University, Gaza - Palestine. Chapter 3 Experiments with a Single Factor: The Analysis of Variance

Islamic University, Gaza - Palestine. Chapter 3 Experiments with a Single Factor: The Analysis of Variance Islm Uverst, Gz - Pleste Chpter 3 xpermets wth Sgle Ftor: The Alss of Vre Islm Uverst, Gz - Pleste 3. A xmple Chpter : A sgl-ftor expermet wth two levels of the ftor Cosder sgl-ftor expermets wth levels

More information

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be

More information

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two

More information

Random variables and sampling theory

Random variables and sampling theory Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred

More information

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg

More information

Chapter 3 Supplemental Text Material

Chapter 3 Supplemental Text Material S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use

More information

Modeling uncertainty using probabilities

Modeling uncertainty using probabilities S 1571 Itroduto to I Leture 23 Modelg uertty usg probbltes Mlos Huskreht mlos@s.ptt.edu 5329 Seott Squre dmstrto Fl exm: Deember 11 2006 12:00-1:50pm 5129 Seott Squre Uertty To mke dgost feree possble

More information

Global Journal of Engineering and Technology Review

Global Journal of Engineering and Technology Review Glol Jourl of Egeerg d eholog Revew Jourl homepge: http://gjetr.org/ Glol Jourl of Egeerg d eholog Revew () 85 9 (06) Applto of Cojugte Grdet Method wth Cu No- Poloml Sple Sheme for Solvg wo-pot Boudr

More information

Numerical Differentiation and Integration

Numerical Differentiation and Integration Numerl Deretto d Itegrto Overvew Numerl Deretto Newto-Cotes Itegrto Formuls Trpezodl rule Smpso s Rules Guss Qudrture Cheyshev s ormul Numerl Deretto Forwrd te dvded deree Bkwrd te dvded deree Ceter te

More information

GENERALIZED OPERATIONAL RELATIONS AND PROPERTIES OF FRACTIONAL HANKEL TRANSFORM

GENERALIZED OPERATIONAL RELATIONS AND PROPERTIES OF FRACTIONAL HANKEL TRANSFORM S. Res. Chem. Commu.: (3 8-88 ISSN 77-669 GENERLIZED OPERTIONL RELTIONS ND PROPERTIES OF FRCTIONL NKEL TRNSFORM R. D. TYWDE *. S. GUDDE d V. N. MLLE b Pro. Rm Meghe Isttute o Teholog & Reserh Bder MRVTI

More information

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1.

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1. SPECIAL MATRICES SYMMETRIC MATRICES Def: A mtr A s symmetr f d oly f A A, e,, Emple A s symmetr Def: A mtr A s skew symmetr f d oly f A A, e,, Emple A s skew symmetr Remrks: If A s symmetr or skew symmetr,

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cpter 7. Smpso s / Rule o Itegrto Ater redg ts pter, you sould e le to. derve te ormul or Smpso s / rule o tegrto,. use Smpso s / rule t to solve tegrls,. develop te ormul or multple-segmet Smpso s / rule

More information

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

ES240 Solid Mechanics Z. Suo. Principal stress. . Write in the matrix notion, and we have

ES240 Solid Mechanics Z. Suo. Principal stress. . Write in the matrix notion, and we have ES4 Sold Mehs Z Suo Prpl stress Prpl Stress Imge mterl prtle stte o stress The stte o stress s xed, but we represet the mterl prtle my wys by uttg ubes deret orettos For y gve stte o stress, t s lwys possble

More information

Analele Universităţii din Oradea, Fascicula: Protecţia Mediului, Vol. XIII, 2008

Analele Universităţii din Oradea, Fascicula: Protecţia Mediului, Vol. XIII, 2008 Alele Uverstăţ d Orde Fsul: Proteţ Medulu Vol. XIII 00 THEORETICAL AND COMPARATIVE STUDY REGARDING THE MECHANICS DISPLASCEMENTS UNDER THE STATIC LOADINGS FOR THE SQUARE PLATE MADE BY WOOD REFUSE AND MASSIF

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

Mathematically, integration is just finding the area under a curve from one point to another. It is b

Mathematically, integration is just finding the area under a curve from one point to another. It is b Numerl Metods or Eg [ENGR 9] [Lyes KADEM 7] CHAPTER VI Numerl Itegrto Tops - Rem sums - Trpezodl rule - Smpso s rule - Rrdso s etrpolto - Guss qudrture rule Mtemtlly, tegrto s just dg te re uder urve rom

More information

CHAPTER 5 Vectors and Vector Space

CHAPTER 5 Vectors and Vector Space HAPTE 5 Vetors d Vetor Spe 5. Alger d eometry of Vetors. Vetor A ordered trple,,, where,, re rel umers. Symol:, B,, A mgtude d dreto.. Norm of vetor,, Norm =,, = = mgtude. Slr multplto Produt of slr d

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

Math 1313 Final Exam Review

Math 1313 Final Exam Review Mth 33 Fl m Revew. The e Compy stlled ew mhe oe of ts ftores t ost of $0,000. The mhe s depreted lerly over 0 yers wth srp vlue of $,000. Fd the vlue of the mhe fter 5 yers.. mufturer hs mothly fed ost

More information

Some Unbiased Classes of Estimators of Finite Population Mean

Some Unbiased Classes of Estimators of Finite Population Mean Itertol Jourl O Mtemtcs Ad ttstcs Iveto (IJMI) E-IN: 3 4767 P-IN: 3-4759 Www.Ijms.Org Volume Issue 09 etember. 04 PP-3-37 ome Ubsed lsses o Estmtors o Fte Poulto Me Prvee Kumr Msr d s Bus. Dertmet o ttstcs,

More information

Recent Progresses on the Simplex Method

Recent Progresses on the Simplex Method Reet Progresses o the Smple Method www.stford.edu/~yyye K.T. L Professor of Egeerg Stford Uversty d Itertol Ceter of Mgemet See d Egeerg Ng Uversty Outles Ler Progrmmg (LP) d the Smple Method Mrkov Deso

More information

Preliminary Examinations: Upper V Mathematics Paper 1

Preliminary Examinations: Upper V Mathematics Paper 1 relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

The Z-Transform in DSP Lecture Andreas Spanias

The Z-Transform in DSP Lecture Andreas Spanias The Z-Trsform DSP eture - Adres Ss ss@su.edu 6 Coyrght 6 Adres Ss -- Poles d Zeros of I geerl the trsfer futo s rtol; t hs umertor d deomtor olyoml. The roots of the umertor d deomtor olyomls re lled the

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt

More information

The Algebraic Least Squares Fitting Of Ellipses

The Algebraic Least Squares Fitting Of Ellipses IOSR Jourl of Mthets (IOSR-JM) e-issn: 78-578 -ISSN: 39-765 Volue 4 Issue Ver II (Mr - Ar 8) PP 74-83 wwwosrjourlsorg he Algebr Lest Squres Fttg Of Ellses Abdelltf Betteb Dertet of Geerl Studes Jubl Idustrl

More information

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process Ru Probablty-Based Ital Captal of the Dsrete-Tme Surplus Proess by Parote Sattayatham, Kat Sagaroo, ad Wathar Klogdee AbSTRACT Ths paper studes a surae model uder the regulato that the surae ompay has

More information

3/20/2013. Splines There are cases where polynomial interpolation is bad overshoot oscillations. Examplef x. Interpolation at -4,-3,-2,-1,0,1,2,3,4

3/20/2013. Splines There are cases where polynomial interpolation is bad overshoot oscillations. Examplef x. Interpolation at -4,-3,-2,-1,0,1,2,3,4 // Sples There re ses where polyoml terpolto s d overshoot oslltos Emple l s Iterpolto t -,-,-,-,,,,,.... - - - Ide ehd sples use lower order polyomls to oet susets o dt pots mke oetos etwee djet sples

More information

Linear Open Loop Systems

Linear Open Loop Systems Colordo School of Me CHEN43 Trfer Fucto Ler Ope Loop Sytem Ler Ope Loop Sytem... Trfer Fucto for Smple Proce... Exmple Trfer Fucto Mercury Thermometer... 2 Derblty of Devto Vrble... 3 Trfer Fucto for Proce

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES Avlble ole t http://sc.org J. Mth. Comput. Sc. 4 (04) No. 05-7 ISSN: 97-507 SUM PROPERTIES OR THE K-UCAS NUMBERS WITH ARITHMETIC INDEXES BIJENDRA SINGH POOJA BHADOURIA AND OMPRAKASH SIKHWA * School of

More information

Chapter 4: Distributions

Chapter 4: Distributions Chpter 4: Dstrbutos Prerequste: Chpter 4. The Algebr of Expecttos d Vrces I ths secto we wll mke use of the followg symbols: s rdom vrble b s rdom vrble c s costt vector md s costt mtrx, d F m s costt

More information

Volume 35, Issue 2. Allocation of costs to clean up a polluted river: an axiomatic approach. Wilson da C. Vieira Federal University of Vicosa, Brazil

Volume 35, Issue 2. Allocation of costs to clean up a polluted river: an axiomatic approach. Wilson da C. Vieira Federal University of Vicosa, Brazil Volume 35, Issue 2 Alloto of osts to le up polluted rver: xomt pproh Wlso d C. Ver Federl Uversty of Vos, Brzl Abstrt Ths pper proposes method to shre the osts of leg up polluted rver mog the gets loted

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of

More information

Lecture 3: Review of Linear Algebra and MATLAB

Lecture 3: Review of Linear Algebra and MATLAB eture 3: Revew of er Aler AAB Vetor mtr otto Vetors tres Vetor spes er trsformtos Eevlues eevetors AAB prmer Itrouto to Ptter Reoto Rro Guterrez-su Wrht Stte Uverst Vetor mtr otto A -mesol (olum) vetor

More information

INTERPOLATION(2) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek

INTERPOLATION(2) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek ELM Numerl Alss Dr Murrem Merme INTEROLATION ELM Numerl Alss Some of te otets re dopted from Luree V. Fusett Appled Numerl Alss usg MATLAB. rete Hll I. 999 ELM Numerl Alss Dr Murrem Merme Tod s leture

More information

CS321. Introduction to Numerical Methods

CS321. Introduction to Numerical Methods CS Itroducto to Numercl Metods Lecture Revew Proessor Ju Zg Deprtmet o Computer Scece Uversty o Ketucky Legto, KY 6 6 Mrc 7, Number Coverso A geerl umber sould be coverted teger prt d rctol prt seprtely

More information

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table. CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml

More information

i+1 by A and imposes Ax

i+1 by A and imposes Ax MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 09.9 NUMERICAL FLUID MECHANICS FALL 009 Mody, October 9, 009 QUIZ : SOLUTIONS Notes: ) Multple solutos

More information

SOLVING INITIAL VALUE PROBLEM USING RUNGE-KUTTA 6 th ORDER METHOD

SOLVING INITIAL VALUE PROBLEM USING RUNGE-KUTTA 6 th ORDER METHOD VOL. NO. JULY ISSN 898 ARPN Jourl o Egeerg d Appled Sees As Reserh Pulshg Networ ARPN. All rghts reserved. www.rpourls.om SOLVING INITIAL VALUE PROBLEM USING RUNGEUTTA th ORDER METHOD As Fdhl As AlShmmr

More information

(This summarizes what you basically need to know about joint distributions in this course.)

(This summarizes what you basically need to know about joint distributions in this course.) HG Ot. ECON 430 H Extra exerses for o-semar week 4 (Solutos wll be put o the et at the ed of the week) Itroduto: Revew of multdmesoal dstrbutos (Ths summarzes what you basally eed to kow about jot dstrbutos

More information

Available online through

Available online through Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo

More information

Z = = = = X np n. n n. npq. npq pq

Z = = = = X np n. n n. npq. npq pq Stt 4, secto 4 Goodess of Ft Ctegory Probbltes Specfed otes by Tm Plchowsk Recll bck to Lectures 6c, 84 (83 the 8 th edto d 94 whe we delt wth populto proportos Vocbulry from 6c: The pot estmte for populto

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

PAIR OF STRAIGHT LINES. will satisfy L1 L2 0, and thus L1 L. 0 represent? It is obvious that any point lying on L 1

PAIR OF STRAIGHT LINES. will satisfy L1 L2 0, and thus L1 L. 0 represent? It is obvious that any point lying on L 1 LOCUS 33 Seto - 3 PAIR OF STRAIGHT LINES Cosder two les L L Wht do ou thk wll L L represet? It s ovous tht pot lg o L d L wll stsf L L, d thus L L represets the set of pots osttutg oth the les,.e., L L

More information

Systems of second order ordinary differential equations

Systems of second order ordinary differential equations Ffth order dgolly mplct Ruge-Kutt Nystrom geerl method solvg secod Order IVPs Fudzh Isml Astrct A dgolly mplct Ruge-Kutt-Nystróm Geerl (SDIRKNG) method of ffth order wth explct frst stge for the tegrto

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

Stats & Summary

Stats & Summary Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product

More information

Matrix. Definition 1... a1 ... (i) where a. are real numbers. for i 1, 2,, m and j = 1, 2,, n (iii) A is called a square matrix if m n.

Matrix. Definition 1... a1 ... (i) where a. are real numbers. for i 1, 2,, m and j = 1, 2,, n (iii) A is called a square matrix if m n. Mtrx Defto () s lled order of m mtrx, umer of rows ( 橫行 ) umer of olums ( 直列 ) m m m where j re rel umers () B j j for,,, m d j =,,, () s lled squre mtrx f m (v) s lled zero mtrx f (v) s lled detty mtrx

More information

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE G Tstsshvl DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE (Vol) 008 September DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE GSh Tstsshvl e-ml: gurm@mdvoru 69004 Vldvosto Rdo str 7 sttute for Appled

More information

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN Euroe Jour of Mthemtcs d omuter Scece Vo. No. 6 ISSN 59-995 ISSN 59-995 ON AN INVESTIGATION O THE MATRIX O THE SEOND PARTIA DERIVATIVE IN ONE EONOMI DYNAMIS MODE S. I. Hmdov Bu Stte Uverst ABSTRAT The

More information

CHAPTER 6 CURVE FITTINGS

CHAPTER 6 CURVE FITTINGS CHAPTER 6 CURVE FITTINGS Chpter 6 : TOPIC COVERS CURVE FITTINGS Lest-Squre Regresso - Ler Regresso - Poloml Regresso Iterpolto - Newto s Dvded-Derece Iterpoltg Polomls - Lgrge Iterpoltg Polomls - Sple

More information

Section 7.2 Two-way ANOVA with random effect(s)

Section 7.2 Two-way ANOVA with random effect(s) Secto 7. Two-wy ANOVA wth rdom effect(s) 1 1. Model wth Two Rdom ffects The fctors hgher-wy ANOVAs c g e cosdered fxed or rdom depedg o the cotext of the study. or ech fctor: Are the levels of tht fctor

More information

Abhilasha Classes Class- XII Date: SOLUTION (Chap - 9,10,12) MM 50 Mob no

Abhilasha Classes Class- XII Date: SOLUTION (Chap - 9,10,12) MM 50 Mob no hlsh Clsses Clss- XII Dte: 0- - SOLUTION Chp - 9,0, MM 50 Mo no-996 If nd re poston vets of nd B respetvel, fnd the poston vet of pont C n B produed suh tht C B vet r C B = where = hs length nd dreton

More information

Design maintenanceand reliability of engineering systems: a probability based approach

Design maintenanceand reliability of engineering systems: a probability based approach Desg mateaead relablty of egeerg systems: a probablty based approah CHPTER 2. BSIC SET THEORY 2.1 Bas deftos Sets are the bass o whh moder probablty theory s defed. set s a well-defed olleto of objets.

More information

Predicting Survival Outcomes Based on Compound Covariate Method under Cox Proportional Hazard Models with Microarrays

Predicting Survival Outcomes Based on Compound Covariate Method under Cox Proportional Hazard Models with Microarrays Predctg Survvl Outcomes Bsed o Compoud Covrte Method uder Cox Proportol Hzrd Models wth Mcrorrys PLoS ONE 7(10). do:10.1371/ourl.poe.0047627. http://dx.plos.org/10.1371/ourl.poe.0047627 Tkesh Emur Grdute

More information

Level-2 BLAS. Matrix-Vector operations with O(n 2 ) operations (sequentially) BLAS-Notation: S --- single precision G E general matrix M V --- vector

Level-2 BLAS. Matrix-Vector operations with O(n 2 ) operations (sequentially) BLAS-Notation: S --- single precision G E general matrix M V --- vector evel-2 BS trx-vector opertos wth 2 opertos sequetlly BS-Notto: S --- sgle precso G E geerl mtrx V --- vector defes SGEV, mtrx-vector product: r y r α x β r y ther evel-2 BS: Solvg trgulr system x wth trgulr

More information

Comparison of Four Methods for Estimating. the Weibull Distribution Parameters

Comparison of Four Methods for Estimating. the Weibull Distribution Parameters Appled Mathematal Sees, Vol. 8, 14, o. 83, 4137-4149 HIKARI Ltd, www.m-hkar.om http://dx.do.org/1.1988/ams.14.45389 Comparso of Four Methods for Estmatg the Webull Dstrbuto Parameters Ivaa Pobočíková ad

More information

PROBLEM SET #4 SOLUTIONS by Robert A. DiStasio Jr.

PROBLEM SET #4 SOLUTIONS by Robert A. DiStasio Jr. PROBLM ST # SOLUTIONS y Roert. DStso Jr. Q. Prove tht the MP eergy s sze-osstet for two ftely seprted losed shell frgmets. The MP orrelto eergy s gve the sp-ortl ss s: vrt vrt MP orr Δ. or two moleulr

More information

Solutions Manual for Polymer Science and Technology Third Edition

Solutions Manual for Polymer Science and Technology Third Edition Solutos ul for Polymer Scece d Techology Thrd Edto Joel R. Fred Uer Sddle Rver, NJ Bosto Idols S Frcsco New York Toroto otrel Lodo uch Prs drd Cetow Sydey Tokyo Sgore exco Cty Ths text s ssocted wth Fred/Polymer

More information

An Extended Mixture Inverse Gaussian Distribution

An Extended Mixture Inverse Gaussian Distribution Avlble ole t htt://wwwssstjscssructh Su Sudh Scece d Techology Jourl 016 Fculty o Scece d Techology, Su Sudh Rjbht Uversty A Eteded Mture Iverse Guss Dstrbuto Chookt Pudrommrt * Fculty o Scece d Techology,

More information

Almost Unbiased Estimation of the Poisson Regression Model

Almost Unbiased Estimation of the Poisson Regression Model Ecoometrcs Worg Pper EWP0909 ISSN 485-644 Deprtmet of Ecoomcs Almost Ubsed Estmto of the Posso Regresso Model Dvd E. Gles Deprtmet of Ecoomcs, Uversty of Vctor Vctor, BC, Cd V8W Y & Hu Feg Deprtmet of

More information

The. time, transport in Žilina, stops. Introduction. was: model of. stopss in order to. design an

The. time, transport in Žilina, stops. Introduction. was: model of. stopss in order to. design an Modellg pssegers rrvls t publc trsport stops Ľudml Jáošíková, Mrt lvík Dept of Trsportto Networks, Fcult of Mgemet cece d Iformtcs, Uverst of Žl, Uverztá, 6 Žl, lovk Republc; Phoe (4-4) 53 4, F (4-4) 565

More information

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index CROATICA CHEMICA ACTA CCACAA ISSN 00-6 e-issn -7X Crot. Chem. Act 8 () (0) 9 0. CCA-5 Orgl Scetfc Artcle Bod Addtve Modelg 5. Mthemtcl Propertes of the Vrble Sum Edeg Ide Dmr Vukčevć Fculty of Nturl Sceces

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

Exercise # 2.1 3, 7, , 3, , -9, 1, Solution: natural numbers are 3, , -9, 1, 2.5, 3, , , -9, 1, 2 2.5, 3, , -9, 1, , -9, 1, 2.

Exercise # 2.1 3, 7, , 3, , -9, 1, Solution: natural numbers are 3, , -9, 1, 2.5, 3, , , -9, 1, 2 2.5, 3, , -9, 1, , -9, 1, 2. Chter Chter Syste of Rel uers Tertg Del frto: The del frto whh Gve fte uers of dgts ts del rt s lled tertg del frto. Reurrg ( o-tertg )Del frto: The del frto (No tertg) whh soe dgts re reeted g d g the

More information

Current Programmed Control (i.e. Peak Current-Mode Control) Lecture slides part 2 More Accurate Models

Current Programmed Control (i.e. Peak Current-Mode Control) Lecture slides part 2 More Accurate Models Curret Progred Cotrol.e. Pek Curret-Mode Cotrol eture lde prt More Aurte Model ECEN 5807 Drg Mkovć Sple Frt-Order CPM Model: Sury Aupto: CPM otroller operte delly, Ueful reult t low frequee, well uted

More information

A Novel Composite-rotating Consensus for Multi-agent System

A Novel Composite-rotating Consensus for Multi-agent System d Itertol Symposum o Computer, Commuto, Cotrol d Automto (CA ) A Novel Composte-rottg Cosesus for Mult-get System Gu L Shool of Aerouts d Astrouts Uversty of Eletro See d ehology of Ch ChegDu, Ch lgu@uestedu

More information

Differential Entropy 吳家麟教授

Differential Entropy 吳家麟教授 Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set

More information

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005 Seto 2:00 ~ 2:50 pm Thursday Marylad 202 Sep. 29, 2005. Homework assgmets set ad 2 revews: Set : P. A box otas 3 marbles, red, gree, ad blue. Cosder a expermet that ossts of takg marble from the box, the

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

COMPLEX NUMBERS AND DE MOIVRE S THEOREM COMPLEX NUMBERS AND DE MOIVRE S THEOREM OBJECTIVE PROBLEMS. s equl to b d. 9 9 b 9 9 d. The mgr prt of s 5 5 b 5. If m, the the lest tegrl vlue of m s b 8 5. The vlue of 5... s f s eve, f s odd b f s eve,

More information

SOLUTION OF TWO DIMENSIONAL FRACTIONAL ORDER VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS

SOLUTION OF TWO DIMENSIONAL FRACTIONAL ORDER VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS Jourl of Al-Nhr Uversty Vol. (4), Deeber, 009,.85-89 See SOLUTION OF TWO DIMENSIONAL FRACTIONAL ORDER VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS Mh A. Mohed * d Fdhel S. Fdhel ** * Dertet of Mthets, Ib-Al-Hth

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

CHAPTER 7 SOLVING FUZZY LINEAR FRACTIONAL PROGRAMMING PROBLEM BY USING TAYLOR'S SERIES METHOD

CHAPTER 7 SOLVING FUZZY LINEAR FRACTIONAL PROGRAMMING PROBLEM BY USING TAYLOR'S SERIES METHOD 67 CHAPTER 7 SOLVING FUZZY LINEAR FRACTIONAL PROGRAMMING PROBLEM BY USING TAYLOR'S SERIES METHOD 7. INTRODUCTION The eso mers the setors le fl ororte lg routo lg mretg me seleto uversty lg stuet mssos

More information

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases Itertol Jourl of Advced Reserch Physcl Scece (IJARPS) Volume, Issue 5, September 204, PP 6-0 ISSN 2349-7874 (Prt) & ISSN 2349-7882 (Ole) www.rcourls.org Alytcl Approch for the Soluto of Thermodymc Idettes

More information

Chapter 12-b Integral Calculus - Extra

Chapter 12-b Integral Calculus - Extra C - Itegrl Clulus Cpter - Itegrl Clulus - Etr Is Newto Toms Smpso BONUS Itroduto to Numerl Itegrto C - Itegrl Clulus Numerl Itegrto Ide s to do tegrl smll prts, lke te wy we preseted tegrto: summto. Numerl

More information

Cooper and McGillem Chapter 4: Moments Linear Regression

Cooper and McGillem Chapter 4: Moments Linear Regression Cooper d McGllem Chpter 4: Momets Ler Regresso Chpter 4: lemets of Sttstcs 4-6 Curve Fttg d Ler Regresso 4-7 Correlto Betwee Two Sets of Dt Cocepts How close re the smple vlues to the uderlg pdf vlues?

More information

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Fll 4 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I Sxth Grde Buld # 7 :!l. ':S.,. (6)()=_ 66 + () = 6 + ()= 88(6)= e :: : : c f So! G) Use the box d whsk plot to sw questos bout the dt ovt the ut of esure usg jjj [ low low ed upp upp proportos. c= 8 =?c

More information

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss

More information

Density estimation II

Density estimation II CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg

More information

Sequences and summations

Sequences and summations Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums Roerto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 2 Rem sums Wht you eed to kow lredy: The defto of re for rectgle. Rememer tht our curret prolem s how to compute the re of ple rego

More information