A MODIFIED CHI-SQUARED GOODNESS-OF-FIT TEST FOR THE KUMARASWAMY GENERALIZED INVERSE WEIBULL DISTRIBUTION AND ITS APPLICATIONS
|
|
- Arron Griffin Hood
- 5 years ago
- Views:
Transcription
1 Jourl of Sscs: Advces Theory d Applcos Volume 6 Number 06 Pges Avlble hp://scefcdvces.co. DOI: hp://dx.do.org/0.864/js_ A MODIFIED CHI-SQUARED GOODNESS-OF-FIT TEST FOR THE KUMARASWAMY GENERALIZED INVERSE WEIBULL DISTRIBUTION AND ITS APPLICATIONS HAFIDA GOUAL d NACIRA SEDDIK-AMEUR Lborory of Probbly d Sscs Uversy of Bdj Mokhr Ab Alger e-ml: hfd-06@homl.fr crseddk@yhoo.fr Absrc I hs pper we hve proposed d sudy ew fve prmeer geerlzed verse Webull model h s bsed o he cumulve dsrbuo fuco of Kumrswmy [4] dsrbuo. The mporce of hs model les s bly o model moooe d o-moooe flure re fucos whch re que commo o lfeme d lyss d relbly. We prese ew goodess-of-f es proposed by Bgdovcus d Nkul [] for he Kumrswmy geerlzed verse Webull model he cse of cesored d. The mehod of mxmum lkelhood s used o esme he model prmeers. We llusre he model d he proposed es by pplcos o wo rel d ses. 00 Mhemcs Subjec Clssfco: 60E5 6E0 6F 6G0 6N05. Keywords d phrses: cesored d geerlzed verse Webull dsrbuo formo mrx Kumrswmy dsrbuo mxmum lkelhood esmo Nkul Ro Robso ssc. Receved December Scefc Advces Publshers
2 76 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR. Iroduco I he survvl d relbly lyss he observed d re frequely complee (cesorg mes). Therefore s ecessry o cosder es of he cesored flure mes d. I 0 Bgdovcus d Nkul developed he de of Akrs [] of comprg observed d expeced umbers of flures me ervls. The choce of rdom groupg ervls used re cosdered s d fucos roducg modfed ch-squred es Y whch s well dped o cesored flure me d. To gve more objecve ssessmes of he seleced model of f he Y ssc s bsed o he mxmum lkelhood esmo (MLE) d he Fsher mrx whch mesure he formo bou he prmeers coed he model chose. I he rece yers severl crero sscs d her pplcos re cosdered such s Hghgh d Nkul [3] Gervlle-Réche e l. [7] Voov e l. [3] Bgdovcus e l. [4] Nkul d Tr [4] Goul d Seddk-Ameur [9]. We roduce hs pper he geerlzed verse Webull (GIW) model bsed o he cumulve dsrbuo fuco of Kumrswmy [4] d clled Kumrswmy geerlzed verse Webull (Kum-GIW) dsrbuo. Tll ow he Kumrswmy dsrbuo hs bee fdg dffere res of pplcos such s survvl lyss relbly bologcl d hydrologcl d Sulo e l. [9] Psco e l. [6] Cordero e l. [5] Ndrjh e l. [8] Cordero e l. [6] Shhbz e l. [30]. O he oher hd he geerlzed verse Webull (GIW) dsrbuo proposed by de Gusmão e l. [7] s flexble model whch c descrbe d predc he flure mes of much rel sysems. The mporce of he ew Kum-GIW dsrbuo les s bly o model mooocy o-mooocy d shpe flure re fucos whch re que commo o lfeme d lyss d relbly. The
3 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 77 purpose of hs pper s o cosruc d lyze he geerlzed Nkul- Ro-Robso goodess-of-f ssc es Y (Bgdovcus d Nkul [] Bgdovcus d Nkul [3]) for he Kumrswmy geerlzed verse Webull (Kum-GIW) dsrbuo boh of complee d cesored d. We roduce he Kum-GIW dsrbuo d we dscuss he shpes of he hzrd re fuco Seco. I Seco 3 we clcule he mxmum lkelhood esmors of he Kum-GIW prmeers he cse of cesored d. We defe Seco 4 he ew goodess-of-f ssc es Y d he vldo of our ew model s vesged Seco 5. Flly he mporce of he proposed model s llusred by wo rel d ses Seco 6.. Kumrswmy Geerlzed Iverse Webull Dsrbuo The geerlzed verse Webull (GIW) dsrbuo s proposed by Gusmão e l. [7]. Ths dsrbuo rses s rcble lfeme model curl sceces lfe esg d relbly. The rdom vrble T follows he geerlzed verse Webull dsrbuo f s cumulve dsrbuo fuco s gve by FGIW ( ) exp α T θ γ θ ( α γ) > 0 > 0. Is probbly desy fuco s ( ) ( ) + exp α f GIW θ γα γ > 0 θ > 0. We propose hs pper exeso of he geerlzed verse Webull dsrbuo bsed o he fmly of geerlzed Kumrswmy dsrbuos (deoed Kum-G) roduced by Cordero d de Csro [6] d Ndrjh e l. [8]. The Kumrswmy (Kum) dsrbuo s o very commo o sscs d hs bee lle explored he
4 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR 78 sscl lerure. Is cumulve dsrbuo fuco s ( ) ( ) b x x F for 0 < < x d where 0 > d 0 > b re shpe prmeers d s desy fuco hs smple form ( ) ( ). b x bx x f A rdom vrble T follows Kumrswmy geerlzed verse Webull (Kum-GIW) dsrbuo f he cumulve dsrbuo fuco s gve s ( ) ( ) 0 0 exp > γ α θ > α γ θ T b b F where > > > b re he shpe prmeers 0 > α s he scle prmeer d 0 > γ s he shf prmeer. Is survvl fuco s ( ) ( ). 0 0 exp > γ α θ > α γ θ T b b S The probbly desy fuco hzrd d cumulve hzrd fucos of he Kum-GIW dsrbuo re ( ) ( ) exp exp + α γ α γ γα θ b b f ( ) 0 > γ α θ T b ( ) ( ) ( ) θ θ θ λ S f ( ) 0 0 exp exp > θ > α γ α γ γα + b ( ) ( ) 0 0 exp l > γ α θ > α γ θ Λ T b b respecvely.
5 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 79 Where he cumulve dsrbuo fuco of geerlzed b Kumrswmy dsrbuo s FKum ( θ ) [ { G() } ] ( G( ) s he cumulve dsrbuo fuco of he geerlzed verse Webull dsrbuo). The grphs of hzrd re fucos re show below (Fgure ) for vrous choces of he prmeers. The flexbly of he Kum-GIW dsrbuos s show her hzrd fuco plos (Fgure ) wh eresg properes: If 0 < d < γ < 4 he s hzrd re hs cup shpe ( -shpe) form. If >. d 0 < γ he s hzrd re s decresg o 0 whch c chrcerze he sysems h mproves (bur- phse or youh bur f morly). If γ 4 he he hzrd re s cresg hs suo chrcerzes sysem whch deerores (phse obsolescece or geg).
6 80 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR
7 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 8 Fgure. Dffere shpes of hzrd re fuco of he Kumrswmy geerlzed verse Webull (Kum-GIW) dsrbuo. 3. Esmg he Prmeers of he Kum-GIW Dsrbuo wh Cesored D Le T be rdom vrble dsrbued wh he vecor of prmeers T θ ( b α γ). Suppose h T re flure mes o-egve d depede d he probbly desy fuco of T belogs o prmerc fmly (Kum-GIW our cse). The cesorg mes C re lso o-egve d ssumed o be rdom smple. Suppose h he d coss of depede observos m( T C ) for. The rgh cesorg s o formve ( C does o deped o θ ). So hs cse we ob he followg expressos of he lkelhood fucos:
8 8 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR δ δ L( θ) f ( θ) S ( θ) δ λ ( θ) S( θ) δ { }. T C The log-lkelhood fuco for he Kum-GIW model s gve by α θ γ ( ) δ [ l + l b + l γ + l + l α ( + ) l( ) l l exp ] l α α + exp γ γ b l( θ) m l + m l b + m l γ + m l + m l α ( + ) l( ) mγα F F ( ) l exp l α α + b exp + γ γ b F C where F d C re he ses of complee d cesored observos respecvely m s he umber of flures. The mxmum lkelhood esmos (MLE s) θ b α γ of ( ) he prmeers re he soluo of he o-ler sysem of score T ( ). fucos ( θ) ( θ) ( θ) ( θ) ( θ) 0 (For more dels see he b α γ 5 Appedx). 4. Goodess-of-f Tes for Rgh Cesored D For esg he goodess-of-f of prmerc fmly of survvl dsrbuo from rgh cesored d Hbb d Thoms [] Hollder d Pe [] cosdered url modfcos of he Nkul-Ro- Robso (NRR) ssc for d whou covres. Also Hjor [3] Hollder d Pe [] cosdered goodess-of-f for prmerc Cox models Gry d Perce [0] Akrs [] d Zhg [33] for ler regresso models. T
9 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 83 Bgdovcus d Nkul [3] gve ch-squred ype goodess-of-fes for cesored d wh possbly me depede covres d cosdered rdom groupg ervls s d fucos. These ess re bsed o mxmum lkelhood esmos for ugrouped d d rdom groupg ervls re cosdered s d fucos. We dp hs es for Kumrswmy geerlzed verse Webull model. Le us cosder he hypohess H0 : s F ( x) F { F ( x θ) x R θ Θ R } 0 0 T s where θ { θ θs} Θ R s ukow s-dmesol vecor prmeer d F 0 s kow dsrbuo fuco. Le us cosder fe me ervl oly sy [ 0 τ] d dvde o k > s smller ervls I ( ] where j j j 0 < 0 < < k < k +. I hs cse he esmed j s gve by ( ( () ˆ )) ( ) j Λ Ej Λ X l θ + θ X( ) j k k l where θ s he mxmum lkelhood esmor of he prmeer θ Λ s he verse of he cumulve hzrd fuco Λ X () s he -h eleme he ordered sscs ( X () X ( )) d E ( + ) Λ ( θ ) + ( X() ); l l Λ θ j re rdom d fucos such s he k ervls chose hve equl expeced umbers of flures e j. The es s bsed o he vecor j j Z T ( Z Zk ) Z j ( U j e j ) j k; U j represe he umbers of observed flures hese ervls.
10 84 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR Uder he hypohess H 0 for prmerc models wh survvl fucos d hzrd res bsoluely couous d o formve cesorg Bgdovcus e l. [4] (Theorems d ) showed h he lm dsrbuo of he ssc es ( ) T Uj ej Y Z Z k + Q U j where s geerlzed verse of he esmed covrce mrx of he vecor Z d T A + A C G CA G I CA C T Q W G W Aj Uj Uj δ X : I k ˆ s ll s s ll ll lj lj j j ( T ) [ ] W W W G g g C C A ( ) l λ( ) l ( θ λ θ ) Clj l ll δ λ θ δ θ θ θ X : Ij k W C A Z l l s l lj j j j s ch-squre wh r rk( ) degrees of freedom. The hypohess s rejeced wh pproxme sgfcce level α f Y > χ α ( r) where χ α ( r) s he qule of ch-squre wh r degrees of freedom. For more dels see Bgdovcus d Nkul [] Bgdovcus d Nkul [3]. j j l T l
11 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT Vldy of he Kum-GIW Model Cesorg Cse The choce of j; j k he cse of Kum-GIW model s obed s follows: j bl exp γα Ej + b l ( exp{ γα X ()}) ( + ) l l k X ( ). To ob he explc form of he modfed ch-squred Y ssc for he Kum-GIW model we mus clcule he mrx C [ c lj] 55 d he Fsher formo mrx ll. (For more dels see he 55 Appedx). 5.. The mrx C The elemes of he esmed mrx C wh C δ : I l λ( θ) l m d j k θ l α α γ M ( ) γ θ C j δ M ( ) : I θ j j C j δ b : Ij γαα γα M ( θ) C3 j δ α M ( ) : I θ j
12 86 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR α α α α l( ) γ l l M ( ) γ θ C4 j δ M ( ) : I θ j α α γ M ( ) θ C5 j δ. γ M ( ) : I θ j So we c clcule he esmed mrx w defed s k w l CljAj Zj l 5 j k. j The we ob he ssc Y for he model of Kumrswmy geerlzed verse Webull s follow: Y k k ( Uj e ) T j + W ll CljCl j Aj W l l 5. U j j j 6. Smulo Sudy 6.. Kum-GIW s prmeers esmo I order o evlue he performce of he mxmu lkelhood mehod of esmg he Kum-GIW prmeers complee d cesored cse we use he R sscl sofwre d Brzl-Borme (BB) lgorhms (Rv e l. [8]). We gve he me smuled mxmum lkelhood esmors vlues b α γ d her me squre errors Tble. Seve smple szes ( d 500) re cosdered. The d were smuled N wh he followg vlues of he prmeers:.5 b.5 α.5 γ 3.
13 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 87 Tble. Me smuled mxmum lkelhood esmors vlue d her me squre errors N SME b SME α SME SME γ SME From Tble we c oe h he mxmum lkelhood mehod of esmg he Kum-GIW prmeers s well perform. The smuled verge bsolue errors of MLE θ ( b α γ ) versus her rue vlue c be fd Tble. For ech smuled smple we compue he verge bsolue of errors s he ol of bsolue dffere of he MLE s gs he rue vlue of he umber of rus N 0000 mes. T
14 Tble. Smule verge bsolue errors of MLE θ versus her rue vlue θ (.5 b.5 α.5 γ 3) T whe d s compleed ( p 0) d he rgh cesorg probbly p 0% 0.5 b b α α γ γ p 0% p 0% p 0% p 0% p 0% p 0% p 0% p 0% p 0% p 0%
15 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT Tble show h ll smuled MLE coverge fser h d hece cofrm he heorem esblshed by Fsher [8] so we demosre h he MLE of Kum-GIW dsrbuo s - cosse he cses of complee d cesored d. 6.. Smuled dsrbuo of Y ssc for Kum-GIW model We es he hypohess H 0 h he ssc Y follows chsqured dsrbuo wh k degrees of freedom. For h we ru 0000 smulos of smples d (smple szes d 500) from Kum-GIW dsrbuo. We group Tble 3 he umber of rejeco s cses of he hypohess H 0 whe Y > χ α ( k ) wh α ( α % α 5% α 0% ) sgfcce levels. Tble 3. Smuled levels of sgfcce for Y ( θ ) es gs her heorecl vlues ( α ) N 0000 α 0. 0 α α Oe c observe h heorecl levels of he ch-squred dsrbuos wh k degrees of freedom mches wh he smuled levels of sgfcce for Y es. Hece we c sy h he es proposed c djus d from Kumrswmy geerlzed verse Webull dsrbuos ccepble mer. For demosrg h he Y ssc follows he lm; chsqured dsrbuo wh k degrees of freedom; we compue N 0000 mes he smuled dsrbuo of Y ( θ ) uder he ull hypohess H 0
16 90 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR wh dffere vlues of prmeers Kum -GIW ( b α γ); for exmple θ (.5 b.5 α.5 γ 3 ) θ (.5 b.5 α.5 γ 3 ) θ (.5 b.5 α.5.5 γ 3 ) θ (.5 b.5 α.5 γ 3 ) θ (.5 b.5 α 0.5 γ 3 ) ( ) θ.5 b.5 α.5 γ.5 d r 3 ervls versus he ch-squred dsrbuo wh k r degree of freedom. Ther hsogrms re represeed Fgure versus he ch-squred dsrbuo wh k degree of freedom. As c see from Fgure oe c observe h he sscl dsrbuo of Y wh dffere vlues of prmeers d dffere umbers k of groupg cells; he lm follows ch-squred wh r k degrees of freedom wh he sscl errors of smulo. We ob he sme resuls for dffere vlue of prmeers d dffere umber of equprobble groupg ervls. I s mes h he lmg dsrbuo of he geerlzed ch-squred Y ssc s dsrbuo free. Ths resul lso esblshed he heorem of Nkul [7 8 9].
17 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 9
18 9 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR
19 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 93 Fgure. Smuled dsrbuo of he Y ssc uder he ull hypohess H 0 wh dffere prmeers of θ versus he ch-squred dsrbuo wh degrees of freedom wh 50 N Applco o rel d To show he pplco of he Kumrswmy geerlzed verse Webull model o survvl d relbly d he cse of cesored d we use wo rel lfe d ses by usg Y ssc.
20 94 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR 6.4. Survvl lyss d cse Pke [7] gve some d from lborory vesgo whch he vgs of rs were ped wh he crcoge DMBA d he umber of dys T ul crcom ppered ws recorded. The d below re for group of 9 rs (Group Pke s pper); he wo observos wh sersks re cesorg mes * 44*. These d re lyzed by Nkul d Tr [5]; where hey suggesed h he probbly plos for wo prmeers Webull dsrbuos he he d re bes fg wh geerlzed Brbum- Suders logsc d he geerlzed Brbum-Suders cuchy dsrbuos. We cosder he hypohese h hese d follow Kum-GIW dsrbuo. Choosg k 6 groupg ervls we fd he vlues of he mxmum lkelhood esmors prmeers: 0.57 b.3884 α γ The vlues of j he frequecy vecor Z re gve he followg ble: j j U J e j Z j The resul of he elemes of he Y ssc re W [ ] d he esmed Fsher s formo mrx I 55 s
21 I d G
22 96 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR By usg he NRR ssc for fve prmeers Kumrswmy geerlzed verse Webull dsrbuo we ob he vlue Y Sce Y.9589 < χ ( 6). 596 (he crcl vlue) we c coclude h he Kumrswmy geerlzed verse Webull (Kum-GIW) model s cocordce wh he ppered me of he crcom of Pke Relbly d (cycles-o-flure of cyldrcl seel specmes) Cycles-o-flure of cyldrcl seel specmes of 4% C el for 30 m 870 C; fer surfce fsh wh corse emery esed by Oo s Rory Uform Bedg Teser 000rpm wh ± 35.6kg/mm. Ths d s obed from Lwless [6] The MLE s of he prmeers geerlzed verse Webull (Kum-GIW) model re b α γ of he Kumrswmy b 0.87 α γ If we choose k 8 ervls he followg ble gves he resuls j j U J e j Z j
23 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 97 The sscl fereces for esg he ull hypoheses h cue cycles-o-flure of cyldrcl seel specmes belog he Kum-GIW model re gve s follows: W [ ] d he esmed Fsher's formo mrx s
24 I
25 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 99 We ob X d Q so he vlue of he ssc es s Y Sce Y 5.09 < χ ( 8) (he crcl vlue) he hypohess H 0 of he Kumrswmy geerlzed verse Webull (Kum-GIW) dsrbuo s well cceped he sgfcce level α Refereces [] M. G. Akrs Perso-ype goodess-of-f ess: The uvre cse Jourl of he Amerc Sscl Assoco 83 (988) -30. [] V. Bgdovcus d M. Nkul Ch-squred ess for geerl compose hypoheses from cesored smples Compes Redus Mhemque 349(3) (0) 9-3. [3] V. Bgdovcus d M. Nkul Ch-squred goodess-of-f es for rgh cesored d Ierol Jourl of Appled Mhemcs d Sscs 4(SI-A) (0) [4] V. Bgdovcus e l. No-prmerc Tess for Cesored D Wley New York 03. [5] G. M. Cordero E. M. M. Oreg d S. Ndrjh The Kumrswmy Webull dsrbuo wh pplco o flure d J. Frkl Is. 349 (00) [6] G. M. Cordero d M. de Csro A ew fmly of geerlzed dsrbuos Jourl of Sscl Compuo d Smulo 8 (0) [7] Felpe R. S. de Gusmão M. Edw M. Oreg Guss d M. Cordero The geerlzed verse Webull dsrbuo S. Ppers 5(3) (009) [8] R. A. Fsher d ohers O propery coecg he χ mesure of dscrepcy wh he mehod of mxmum lkelhood Jourl of he Royl Sscl Socey 87 (98) [9] H. Goul d N. Seddk-Ameur Ch-squred ype es for he AFT-geerlzed verse Webull dsrbuo Commucos Sscs-Theory d Mehods 43(3) (04) [0] Rober J. Gry d Dold A. Perce Goodess-of-f ess for cesored survvl d A. Ss. 3() (985) [] M. G. Hbb d D. R. Thoms Ch-squre goodess-f-f ess for rdomly cesored d The Als of Sscs 4() (986) [] M. Hollder d E. Pe Ch-squre goodess-of-f es for rdomly cesored d JASA 87(48) (99)
26 300 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR [3] N. L. Hjor Goodess of f ess models for lfe hsory d bsed o cumulve hzrd res A. Ss. 8 (990) -58. [4] P. Kumrswmy Geerlzed probbly desy-fuco for double-bouded rdom-processes Jourl of Hydrology 46 (980) [5] J. F. Lwless Sscl Models d Mehods for Lfeme D (Secod Edo) Wley New York 003. [6] J. F. Lwless Relbly Lfe Tesg d he Predco of Servce Lves: For Egeers d Scess by Sm C. Suders Sprger Seres Sscs 75() (007) 60. [7] L. Gervlle-Réche M. S. Nkul R. Thr e l. O relbly pproch d sscl ferece demogrphy Jourl of Relbly d Sscl Sudes 5 (0) [8] S. Ndrjh G. M. Cordero d E. M. M. Oreg Geerl resuls for he Kumrswmy-G dsrbuo Jourl of Sscl Compuo d Smulo 8(7) (0) [9] M. S. Nkul L. Gervlle-Réche d X. Q. Tr O ch-squred goodess-of-f es for ormly I V. Couller L. Gervlle-Réche H. Huber-Crol N. Lmos d M. Mesbh Edors Sscl Models d Mehods for Relbly d Survvl Alyss Wley Ole Lbrry New York (03) 3-7. [0] M. S. Nkul Ch-squred es for ormly proceedgs of he Ierol Vlus Coferece o Probbly Theory d Mhemcl Sscs (973) 9-. [] M. S. Nkul Ch-squred es for couous dsrbuos wh shf d scl prmeers Theory of Probbly d s Applcos 8 (973b) [] M. S. Nkul O ch-squred es for couous dsrbuos Theory of Probbly d s Applcos 9 (973c) [3] M. Nkul d F. Hghgh A ch-squred es for he geerlzed power webull fmly for he hed-d-eck ccer cesored d Jourl of Mhemcl Sceces 33(3) (006) [4] M. S. Nkul d X. Q. Tr O ch-squred esg ccelered rls Ierol Jourl of Performbly Egeerg 0() (04) [5] M. S. Nkul d X. Q. Tr Ch-squred goodess-of-f ess for geerlzed Brbum-Suders models for rgh cesored d d s relbly pplcos Jourl of Relbly: Theory & Applco Elecroc Jourl of Ierol Groups o Relbly 8(#) (03) 7-0. [6] M. A. R. Psco E. M. M. Oreg d G. M. Cordero The Kumrswmy geerlzed gmm dsrbuo wh pplco survvl lyss Sscl Mehodology 8 (0) [7] M. C. Pke A mehod of lyss of cer clss of expermes crcogeess Bomercs (966) 4-6.
27 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 30 [8] V. Rv d D. Glber Pul BB: A R pckge for solvg lrge sysem of oler equos d for opmzg hgh-dmesol oler objecve fuco Jourl of Sscl Sofwre 3(04) (009) -6. [9] H. Sulo J. Leo d M. Bourgugo The Kumrswmy brbum-sders dsrbuo J. S. Theory Prc. 6 (0) [30] M. Q. Shhbz S. Shhbz d N. S. Bu The Kumrswmy verse-webull dsrbuo Pks J. Ss. Oper. Res. 8 (0) [3] V. Voov M. S. Nkul d N. Blkrsh Ch-Squred Goodess of F Tess wh Applcos Acdemc Press New York 03. [3] V. Voov N. Py N. Shpkov d Y. Voov Goodess-of-f ess for he power geerlzed Webull probbly dsrbuo Commucos Sscs- Smulo d Compuo 4(5) (03) [33] B. Zhg A ch-squred goodess-of-f es for logsc regresso models bsed o csecorol d Bomerk 86(3) (999) g
28 30 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR Appedx The score fucos re l ( θ) l( θ) r α γ M ( θ) γα + ( ) r b M ( θ) F F l ( θ) b l( θ) b r b + F + b C α γ M ( θ) M ( θ) l( M ( θ) ) + l( M ( θ) ) C l ( θ) α l( θ) α r α rγα γα M ( θ) + ( b ) M ( θ) F F + b C γα M ( θ) M ( θ) l ( θ) l( θ) r F F l( ) rγα ( l( α) l( )) + γα [ l( α) l( )] M ( θ) ( b ) M ( θ) F γα [ l( α) l( )] M ( θ) + b M ( θ) C l ( θ) γ l( θ) r rα γ γ F + ( b ) F α M ( θ) + b M ( θ) C α M ( θ) M ( θ) α where M ( θ) exp γ.
29 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 303 Fsher formo mrx The elemes of he Fsher formo mrx l λ( θ) l λ( θ) ll whll δ θ θ l l re α α γ M ( ) γ θ δ M ( ) θ α α γ M ( ) γ θ δ b M ( ) θ α α γ M ( ) γ θ γαα γα M ( θ) 3 δ M ( ) ( ) θ α M θ α α γ M ( ) γ θ 4 δ M ( ) θ α α α α l( ) γ l l M ( ) γ θ M ( θ) α α α α γ M ( ) M ( ) γ θ γ θ 5 δ M ( ) ( ) θ γ M θ
30 304 HAFIDA GOUAL d NACIRA SEDDIK-AMEUR δ b γαα γα M ( θ) 3 δ b α M ( ) θ α α α α l( ) γ l l M ( ) γ θ 4 δ b M ( ) θ α α γ M ( ) θ 5 δ b γ M ( ) θ γαα γα M ( θ) 33 δ α M ( ) θ γαα γα M ( θ) 34 δ α M ( ) θ α α α α l( ) γ l l M ( ) γ θ M ( θ) α α ( ) ( ) γ M θ γαα γα M θ 35 δ M ( ) ( ) M α θ γ θ α α α α l( ) γ l l M ( ) γ θ 44 δ M ( ) θ
31 A MODIFIED CHI-SQUARED GOODNESS-OF-FIT 305 α α α α l( ) γ l l M ( ) γ θ 45 δ M ( ) θ α α γ M ( ) θ γ M ( θ) α α γ M ( ) θ 55 δ γ M ( ) θ.
Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationDecompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)
. Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.
More informationAn improved Bennett s inequality
COMMUNICATIONS IN STATISTICS THEORY AND METHODS 017,VOL.0,NO.0,1 8 hps://do.org/10.1080/0361096.017.1367818 A mproved Bee s equly Sogfeg Zheg Deprme of Mhemcs, Mssour Se Uversy, Sprgfeld, MO, USA ABSTRACT
More informationG1-Renewal Process as Repairable System Model
G-Reewl Process s Reprble Sysem Model M.P. Kmsky d V.V. Krvsov Uversy of Mryld College Prk USA Ford Moor Compy Derbor USA Absrc Ths pper cosders po process model wh moooclly decresg or cresg ROCOF d he
More informationThe Infinite NHPP Software Reliability Model based on Monotonic Intensity Function
Id Jourl of Scece d Techology, Vol 8(4), DOI:.7485/js/25/v84/68342, July 25 ISSN (Pr) : 974-6846 ISSN (Ole) : 974-5645 The Ife Sofwre Relly Model sed o Moooc Iesy Fuco Te-Hyu Yoo * Deprme of Scece To,
More informationChapter 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 informationUnscented Transformation Unscented Kalman Filter
Usceed rsformo Usceed Klm Fler Usceed rcle Fler Flerg roblem Geerl roblem Seme where s he se d s he observo Flerg s he problem of sequell esmg he ses (prmeers or hdde vrbles) of ssem s se of observos become
More informationINTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY
[Mjuh, : Jury, 0] ISSN: -96 Scefc Jourl Impc Fcr: 9 ISRA, Impc Fcr: IJESRT INTERNATIONAL JOURNAL OF ENINEERIN SCIENCES & RESEARCH TECHNOLOY HAMILTONIAN LACEABILITY IN MIDDLE RAPHS Mjuh*, MurlR, B Shmukh
More informationThe Existence and Uniqueness of Random Solution to Itô Stochastic Integral Equation
Appled Mhemcs,, 3, 8-84 hp://dx.do.org/.436/m..379 Pulshed Ole July (hp://www.scrp.org/jourl/m) The Exsece d Uqueess of Rdom Soluo o Iô Sochsc Iegrl Equo Hmd Ahmed Alff, Csh Wg School of Mhemcs d Iformo
More informationModeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25
Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:
More informationIsotropic Non-Heisenberg Magnet for Spin S=1
Ierol Jourl of Physcs d Applcos. IN 974- Volume, Number (, pp. 7-4 Ierol Reserch Publco House hp://www.rphouse.com Isoropc No-Heseberg Mge for p = Y. Yousef d Kh. Kh. Mumov.U. Umrov Physcl-Techcl Isue
More informationA NEW FIVE-POINT BINARY SUBDIVISION SCHEME WITH A PARAMETER
Jourl of ure d Appled Mhemcs: Advces d Applcos Volume 9 Numer ges -9 Avlle hp://scefcdvcesco DOI: hp://dxdoorg/6/ms_9 A NEW FIVE-OINT BINARY UBDIVIION CHEME WITH A ARAMETER YAN WANG * d HIMING LI chool
More information4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula
NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul
More informationApplication of Multiple Exp-Function Method to Obtain Multi-Soliton Solutions of (2 + 1)- and (3 + 1)-Dimensional Breaking Soliton Equations
Amerc Jourl of Compuol Appled Mhemcs: ; (: 4-47 DOI:.593/j.jcm..8 Applco of Mulple Exp-Fuco Mehod o Ob Mul-Solo Soluos of ( + - (3 + -Dmesol Breg Solo Equos M. T. Drvsh,*, Mlheh Njf, Mohmmd Njf Deprme
More information4. Runge-Kutta Formula For Differential Equations
NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul
More informationIntegral Equations and their Relationship to Differential Equations with Initial Conditions
Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationThe Cauchy Problem for the Heat Equation with a Random Right Part from the Space
Appled Mhemcs, 4, 5, 38-333 Publshed Ole Augus 4 ScRes hp://wwwscrporg/jourl/m hp://dxdoorg/436/m4556 The Cuch Problem for he He Equo wh Rdom Rgh Pr from he Spce Sub ϕ ( Ω Yur Kozcheo, A Slv-Tlshch Deprme
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationCOMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION
COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue
More informationSTOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION
The Bk of Thld Fcl Isuos Polcy Group Que Models & Fcl Egeerg Tem Fcl Mhemcs Foudo Noe 8 STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION. ก Through he use of ordry d/or prl deres, ODE/PDE c rele
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More informationThe Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting
Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad
More informationComparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution
Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm
More informationMultivariate Regression: A Very Powerful Forecasting Method
Archves of Busess Reserch Vol., No. Pulco De: Jue. 5, 8 DOI:.78/r..7. Vslooulos. (8). Mulvre Regresso: A Very Powerful Forecsg Mehod. Archves of Busess Reserch, (), 8. Mulvre Regresso: A Very Powerful
More informationBEST PATTERN OF MULTIPLE LINEAR REGRESSION
ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS
More informationAvailable 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 informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More informationEstimation and Optimum Constant-Stress Partially Accelerated Life Test Plans for Pareto Distribution of the Second Kind with Type-I Censoring
Esmo d Opmm Cos-ress Prlly Accelered fe Tes Pls for Preo Dsrbo of he ecod Kd wh Type-I Cesorg By Abdll A. Abdel-Ghly Em H. El-Khodry Al A. Isml 3 Key Words d Phrses: Relbly; Preo dsrbo; prl ccelero; cos-sress;
More informationASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES
ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl
More informationSurvival Prediction Based on Compound Covariate under Cox Proportional Hazard Models
Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae
More informationModified Taylor's Method and Nonlinear Mixed Integral Equation
Uversl Jourl of Iegrl quos 4 (6), 9 wwwpperscecescom Modfed Tylor's Mehod d oler Mxed Iegrl quo R T Moog Fculy of Appled Scece, Umm Al Qurh Uversy Mkh, Kgdom of Sud Ar rmoog_777@yhoocom Asrc I hs pper,
More informationParameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data
Avlble ole wwwsceceeccom Physcs Poce 0 475 480 0 Ieol Cofeece o Mecl Physcs Bomecl ee Pmee smo Hyohess es of wo Neve Boml Dsbuo Poulo wh Mss D Zhwe Zho Collee of MhemcsJl Noml UvesyS Ch zhozhwe@6com Absc
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationAPPLICATION REGRESSION METHOD IN THE CALCULATION OF INDICATORS ECONOMIC RISK
APPLICATIO REGRESSIO METHOD I THE CALCULATIO OF IDICATORS ECOOMIC RISK Ec. PhD Flor ROMA STA Asrc The ojecve of hs Arcle s o show h ecoomc rsk s flueced mulple fcors, d regresso mehod c eslsh he ee of
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationMoments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables
Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.
More informationNONLINEAR SYSTEM OF SINGULAR PARTIAL DIFFERENTIAL EQUATIONS
Jourl of Mhemcl Sceces: dvces d pplcos Volume 43, 27, Pges 3-53 vlble hp://scefcdvces.co. DOI: hp://d.do.org/.8642/ms_72748 OLIER SYSTEM OF SIGULR PRTIL DIFFERETIL EQUTIOS PTRICE POGÉRRD Mhemcs Lborory
More informationICS141: Discrete Mathematics for Computer Science I
Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll
More informationStochastic Hydrology
GEO4-44 Sochsc Hydrology Prof. dr. Mrc F.P. Beres Prof. dr. Frs C. v Geer Deprme of Physcl Geogrphy Urech Uversy Coes Iroduco... 6. Why sochsc hydrology?... 6. Scope d coe of hese lecure oes....3 Some
More informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationHeart pacemaker wear life model based on frequent properties and life distribution*
J. Boedcl Scece d Egeerg,, 3, 375-379 JBSE do:.436/jse..345 Pulshed Ole Aprl (hp://www.scrp.org/jourl/jse/). Her pceer wer lfe odel sed o freque properes d lfe dsruo* Qo-Lg Tog, Xue-Cheg Zou, J Tg, Heg-Qg
More information-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for
Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my
More informationSome Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables
Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3
More informationArea 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 informationCURVE 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 informationInference on One Population Mean Hypothesis Testing
Iferece o Oe Popultio Me ypothesis Testig Scerio 1. Whe the popultio is orml, d the popultio vrice is kow i. i. d. Dt : X 1, X,, X ~ N(, ypothesis test, for istce: Exmple: : : : : : 5'7" (ull hypothesis:
More informationCyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles
Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of
More informationLaplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.
Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o
More informationInternational Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.
www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra
More informationAn Intelligent System for Parking Trailer using Reinforcement Learning and Type 2 fuzzy Logic
Jourl of Elecrcl d Corol Egeerg A Iellge Sysem for Prg Trler usg eforceme Lerg d Type 2 fuzzy Logc Morez Shrf, 2 Mohmmd ez Alm, 3 Seyed Abolfzl Foor,2,3 Deprme of Elecrcl Egeerg,Islmc Azd Uversy obd brch
More informationA note on Turán number Tk ( 1, kn, )
A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco
More informationContinuous Indexed Variable Systems
Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh
More informationP-Convexity Property in Musielak-Orlicz Function Space of Bohner Type
J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg
More informationKey words: Fractional difference equation, oscillatory solutions,
OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg
More informationThe algebraic immunity of a class of correlation immune H Boolean functions
Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales
More informationStat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty
S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.
More informationPredicting 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 informationTechnical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005.
Techc Appedx fo Iveoy geme fo Assemy Sysem wh Poduc o Compoe eus ecox d Zp geme Scece 2005 Lemm µ µ s c Poof If J d µ > µ he ˆ 0 µ µ µ µ µ µ µ µ Sm gumes essh he esu f µ ˆ > µ > µ > µ o K ˆ If J he so
More informationIntroduction 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 informationPolicy optimization. Stochastic approach
Polcy opmzo Sochc pproch Dcree-me Mrkov Proce Sory Mrkov ch Sochc proce over fe e e S S {.. 2 S} Oe ep ro probbly: Prob j - p j Se ro me: geomerc drbuo Prob j T p j p - 2 Dcree-me Mrkov Proce Sory corollble
More informationAn 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 informationThe Products of Regularly Solvable Operators with Their Spectra in Direct Sum Spaces
Advces Pure Mhemcs 3 3 45-49 h://dxdoorg/436/m3346 Pulshed Ole July 3 (h://wwwscrorg/ourl/m) he Producs of Regulrly Solvle Oerors wh her Secr Drec Sum Sces Sohy El-Syed Irhm Derme of Mhemcs Fculy of Scece
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationExpectation and Moments
Her Sr d Joh W. Woods robbl Sscs d Rdom Vrbles or geers 4h ed. erso duco Ic.. ISB: 978----6 Cher 4 eco d omes Secos 4. eced Vlue o Rdom Vrble 5 O he Vld o quo 4.-8 8 4. Codol ecos Codol eco s Rdom Vrble
More informationProbability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract
Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.
More informationIntroduction to Neural Networks Computing. CMSC491N/691N, Spring 2001
Iroduco o Neurl Neorks Compug CMSC49N/69N, Sprg 00 us: cvo/oupu: f eghs: X, Y j X Noos, j s pu u, for oher us, j pu sgl here f. s he cvo fuco for j from u o u j oher books use Y f _ j j j Y j X j Y j bs:
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationFully Fuzzy Linear Systems Solving Using MOLP
World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,
More informationMATRIX AND VECTOR NORMS
Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the
More informationChapter 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 informationSequences 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 informationME 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 informationONE RANDOM VARIABLE F ( ) [ ] x P X x x x 3
The Cumulive Disribuio Fucio (cd) ONE RANDOM VARIABLE cd is deied s he probbiliy o he eve { x}: F ( ) [ ] x P x x - Applies o discree s well s coiuous RV. Exmple: hree osses o coi x 8 3 x 8 8 F 3 3 7 x
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More information4.8 Improper Integrals
4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls
More informationA New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering
Sed Orders for Reprs o reprs@behmscece.e The Ope Cyberecs & Sysemcs Jourl, 04, 8, 89-834 89 Ope ccess New NFIS Model bsed o Mul-Ipu Hmcher T-orm Subrc Cluserg Feg-Y Zhg Zh-Go Lo * Deprme of Mgeme, Gugx
More informationXidian University Liu Congfeng Page 1 of 49
dom Sgl Processg Cher4 dom Processes Cher 4 dom Processes Coes 4 dom Processes... 4. Deo o dom Process... 4. Chrcerzo o dom Process...4 4.. ol Chrcerzo o dom Process...4 4.. Frs-Order Deses o dom Process...5
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationMTH 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 informationOn Several Inequalities Deduced Using a Power Series Approach
It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty
More informationRandom 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 informationApproximations of Definite Integrals
Approximtios of Defiite Itegrls So fr we hve relied o tiderivtives to evlute res uder curves, work doe by vrible force, volumes of revolutio, etc. More precisely, wheever we hve hd to evlute defiite itegrl
More informationGeneralisation on the Zeros of a Family of Complex Polynomials
Ieol Joul of hemcs esech. ISSN 976-584 Volume 6 Numbe 4. 93-97 Ieol esech Publco House h://www.house.com Geelso o he Zeos of Fmly of Comlex Polyomls Aee sgh Neh d S.K.Shu Deme of hemcs Lgys Uvesy Fdbd-
More informatione t dt e t dt = lim e t dt T (1 e T ) = 1
Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie
More informationThe Lucas congruence for Stirling numbers of the second kind
ACTA ARITHMETICA XCIV 2 The Luc cogruece for Srlg umber of he ecod kd by Robero Sáchez-Peregro Pdov Iroduco The umber roduced by Srlg 7 h Mehodu dfferel [], ubequely clled Srlg umber of he fr d ecod kd,
More informationHow to explore replicator equations? G.P. Karev
How o explore replcor equos? GP Krev Locheed r SD Nol Isue of Helh Bldg 38 R 5N5N 86 Rocvlle Pe Behes D 2894 US E-l: rev@clhgov src Replcor equos RE) re og he sc ools hecl heory of seleco d evoluo We develop
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationReview of the Riemann Integral
Chpter 1 Review of the Riem Itegrl This chpter provides quick review of the bsic properties of the Riem itegrl. 1.0 Itegrls d Riem Sums Defiitio 1.0.1. Let [, b] be fiite, closed itervl. A prtitio P of
More informationWeek 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)
Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he
More informationThe Properties of Probability of Normal Chain
I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co
More informationSoo 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 informationPatterns of Continued Fractions with a Positive Integer as a Gap
IOSR Jourl of Mthemtcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X Volume, Issue 3 Ver III (My - Ju 6), PP -5 wwwosrjourlsorg Ptters of Cotued Frctos wth Postve Iteger s G A Gm, S Krth (Mthemtcs, Govermet
More informationAnalytical 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 informationBianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity
Ieraoal Joural of Mahemacs esearch. IN 0976-50 Volume 6, Number (0), pp. 6-7 Ieraoal esearch Publcao House hp://www.rphouse.com Bach ype II ff Flud led Cosmologcal Model Geeral elay B. L. Meea Deparme
More informationChapter Simpson s 1/3 Rule of Integration. ( x)
Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use
More informationThe 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 informationAvailable online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article
Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION
More information