Ekpenyong Emmanuel John and Gideon Sunday N. x (2.1) International Journal of Statistics and Applied Mathematics 2018; 3(4): 60-64

Size: px
Start display at page:

Download "Ekpenyong Emmanuel John and Gideon Sunday N. x (2.1) International Journal of Statistics and Applied Mathematics 2018; 3(4): 60-64"

Transcription

1 Itrtol Jourl of Sttstcs d Appld Mtmtcs 8; ISSN Mts 8; Stts & Mts Rcvd Accptd Ekpyo Emmul Jo Dprtmt of Sttstcs Mcl Okpr Uvrsty of Arcultur Umudk Nr Gdo Sudy N Dprtmt of Sttstcs Ab Stt Polytcc Ab Nr Bys stmto of t sp prmtr of burr typ XII dstrbuto wt rlsd squrd rror loss d l loss fuctos udr som pror dstrbutos Ekpyo Emmul Jo d Gdo Sudy N Abstrct I ts ppr w obt som clsscl d Bys stmtors of t sp prmtrs of t Burr Typ XII dstrbuto. T Bys stmto ws md udr rlzd squrd rror loss fucto GSEF d lr potl INEX loss fuctos wt o-formtv pror Jffrys pror d formtv pror Gmm pror. ywords Bys stmto pror dstrbuto postror dstrbuto loss fucto d m squr rror. Itroducto Twlv dffrt forms of dstrbuto fuctos usful modl wd r of prmtl d bolocl dt forstry frctur rouss lf tst oprtol rsk tc wr troducd by Burr 94. Amo ts dstrbutos t Burr Typs III X d XII v ovr tm d mor rcoto d rtr pplcto rsrcs d ltrturs bcus of tr flblty d comptblty wt otr dstrbutos. For stc Burr typs III d XII ppromt svrl dstrbutos t fmls of o-orml dstrbutos s Burr 973; Rodruz 977; Tdkml 98 d Hdrck t l.. Burr Typ X dstrbuto s prtculrly mportt modll procsss wt rdully crs flur rt wtout y boud. It s lso ffctv modll strt dt d lf tm Surls d Pdtt. Svrl studs d b crrd out o Burr Typ XII dstrbuto wc clud u t l 7 Mkdoom d Jfr Solm t l P d Asd Sdu d Aslm 3 Al-Sr t l 4 J 4 d Tsum t l 5.Ts ppr focusd o Bys stmto of t sp prmtr of two-prmtr Burr Typ XII dstrbuto wt rspct to Grlzd Squr Error oss Fucto GSEF d r Epotl INEX loss fucto us Jffry s pror s o-formtv pror d mm pror s formtv pror wt t m of compr t prformc of t stmtors udr t two loss fuctos.. Clsscl Estmto of t Sp Prmtr of Burr Typ XII Dstrbuto Hr w cosdr tr mtods of clsscl stmto t Mmum klood Estmto ME Mmum M Squr Error MSE d t Uform Mmum Vrc Ubsd Estmto UMVUE. t X~Burr Typ XII α β t t probblty dsty fucto pdf of X s v by Corrspodc Ekpyo Emmul Jo Dprtmt of Sttstcs Mcl Okpr Uvrsty of Arcultur Umudk Nr f ; ~6~. wr α s t sp prmtr d β t scl prmtr. It c sly b sow tt. s vld pdf d t lo-lklood fucto s v by

2 Itrtol Jourl of Sttstcs d Appld Mtmtcs I I I I I. Tus w obt t ME s ˆ ME I wr I Rsd d Njm 4 vlutd t Mmum M Squr Error MMSE t clss of stmtors rprstd s. d obtd s.3 E E.4 Rsd d Njm 4 otd tt sc. s t fmly of potl dstrbuto; t mpls tt s Gmm dstrbuto wt prmtrs d α. ~G. α. Hc E d E. Substtut ts pcttos.4 mply d tus t MMSE of s; ˆ MMSE.5 I Bsd o m-scff s torm s ubsd stmt of α sc E d s suc s complt suffct sttstc for α. Trfor t UMVUE of α s v by ˆ UMVUE.6 I Yrmommd d Pzr drvd t M Squr Error MSE of t tr clsscl stmtors d lso sowd tt MSE ˆ MSE ˆ MSE ˆ MMSE UMVUE ME 3. Bys Estmto of t Sp Prmtr of Burr Typ XII Dstrbuto Bys stmto volvs t stmto of ukow prmtr wc s rrdd s rdom vrbl from v probblty dstrbuto. Ts c b crrd out us o-formtv pror dstrbuto Jffry pror or formtv pror dstrbuto of t prmtr of trst cosdrto of obsrvd dt sy... Howvr Jffry pror d mm pror dstrbuto wr cosdrd s o-formtv d formtv pror dstrbutos of t sp prmtr of Burr Typ XII dstrbuto d stmtos wr md udr GSEF d INEX oss Fucto. 3. T Postror Dstrbutos T o-formtv pror dstrbuto for α us Jffry pror s v by I wr s t Fsr formto dfd s 3. If I E 3. Tk t turl lo of. w v ~6~

3 ~6~ Itrtol Jourl of Sttstcs d Appld Mtmtcs I I I I If 3.3 T scod ordr prtl drvtv of 3.3 wt rspct to α vs If 3.4 Substtut 3.4 to 3. mpls I wc furtr mpls tt ; 3.5 obt t postror dsty fucto of α v Jffry s pror s ;... d d I I I I Tus t postror dstrbuto of α v Jffry s pror s Gmm wt prmtrs d. If t pror dstrbuto of s mm wt prmtrs d t t probblty dsty fucto pdf of s v by 3.7 Us 3.7 w obt t postror dstrbuto of s ;... d d I I I I Tus t postror dstrbuto of α v Gmm pror s Gmm wt prmtrs d. 3. Bys Estmto udr GSEF Rsd d Al Gz 4 v t Grlzd Squr Error oss fucto GSEF lθ θ s ˆ ˆ j j ;....3 d obtd stmtor for us t corrspod rsk fucto s θ = Eθ t+ Eθ t+ + Eθ + t + Eθ t+ + Eθ t 3.9 Substtut α for θ d for t 3.9 α = Eα + Eα + + Eα + + Eα + + Eα 3.

4 Itrtol Jourl of Sttstcs d Appld Mtmtcs T rsult 3.6 mpls tt Eα = d w Eα = + c substtut ts momts 3. vs t GSEF w stmtor for α us Jffry s pror α GSEFJ = w + + w + + +k+k + w k+ + w + + +k +k + w k = Γ++j j=o j I+ β j+ Γ Γ+j j=o j I+ β j Γ 3. From 3.8 Eα = +θ d +λ Eα = +θ+θ+ c t GSEF stmtor for α us mm pror +λ α GSEF = +θ w+λ + +θ++θ +θ+k+θ+k +θ++θ w+λ + + w+λ k+ +θ + w+λ + + +θ+k +θ+k +θ++θ w k = Γ+θ++j j=o j I+ β +λ j+ Γ+θ Γ+θ+j j=o j I+ β +λ j Γ+θ Bys Estmto udr INEX Rsd d Sult 5 ppld INEX loss fucto of t form ˆ b ˆ ˆ 3.3 wr b d obtd stmtor for θ us t rsk fucto s E l ˆ 3.4 Substtut α for θ 3.4 w v E l ˆ 3.5 Apply t rsult 3.6 w obtd stmtor for α udr INEX loss fucto wt Jffry s pror s follows; E.. d d 3.6 Substtut w v ˆ INEX j l l l l 3.7 Gv t postror dstrbuto 3.8 t INEX stmtor for α us mm pror s obtd s follows.. d E ~63~ d 3.8

5 Itrtol Jourl of Sttstcs d Appld Mtmtcs INEX l l l ˆ Cocluso I ts work w v b bl to drv t Bys stmtor of t sp prmtr of t Burr typ IX dstrbuto udr squrd rror loss d INEX fuctos wt Jffry s d Gmmα β prors. 5. Rfrcs. A-Sr AY Brt A Mous SA. Mrsl Olk Etdd Burr Typ XII Dstrbuto. Itrtol Jourl of Sttstcs d Probblty Burr I. Cumultv Frqucy Fuctos. Al of Mtmtcl Sttstcs. 94; Burr I. Prmtrs for Grl Systm of Dstrbuto to Mtc Grd of α 3 d α 4 Commucto Sttstcs-Tory d Mtods. 973; Hdrck TC Pt MD S Y. O Smult Uvrt d Multvrt Burr Typ III d Typ XII Dstrbutos. Appld Mtmtcl Scc. ; J DH. Bys Estmto of Burr Typ XII Bsd o Grl Prorssv Typ II Csor. Appld Mtmtcl Sccs. 4; Mkdoom I Jfr A. Bys Estmtos o Burr Typ XII Dstrbuto us Groupd d Uroupd. Austrl Jourl of Bsc d Appld Sccs. ; P H Asd S. Alyss of t Typ II Hybrd Csord Burr Typ XII Dstrbuto udr INEX oss Fucto. Appld Mtmtcl Sccs. ; Rsd HA AAlwy Al-Gz NA. Bys Estmto for t Rlblty Fucto of Prto Typ I Dstrbuto udr Grlzd Squr Error oss Fucto. Itrtol Jourl of Er d Iovtv Tcoloy IJEIT. 4; Rsd HA Al-Gz NA. Bys Estmtors for t Sp Prmtr of Prto Typ I Dstrbuto udr Grlzd Squr Error oss Fucto. Mtmtcl Tory d Modl. 4; Rsd HA Sult AJ. Bys Estmto of t Scl Prmtr for Ivrs Gmm Dstrbuto udr INEX oss Fucto. Itrtol Jourl of Advcd Rsrc. 5; Rodruz RN. A Gud to Burr Typ XII Dstrbutos Bomtrk. 977; Sdu TN Aslm M. Estmto of t Burr Typ VIII Dstrbuto trou Bys Frmwork Advcs Arts Socl Sccs d Educto Rsrc. 3; Solm AA Abd Ell AH Abou-Al NA. Bys Ifrc d Prdcto of Burr Typ XII Dstrbuto for Prorssv Frst Flur Csord Smpl. Itllt Iformto Mmt. ; Surls JG Pdtt J. Ifrc for Rlblty d Strss-Strt for Scld Burr Typ X Dstrbuto ftm Dt Alyss. ; Tdkml PR. A look o t Burr d Rltd Dstrbuto. Itrtol Sttstcl Rvw. 98; AlBldw TH Rsd HA Jsm SH. Us Grlzd Squr oss Fucto to Estmt t Sp Prmtr of t Burr Typ XII Dstrbuto. Itrtol Jourl of Advcd Rsrc. 5; u SJ C YJ C CT. Sttstcl Ifrc Bsd O Prorssvly Csord Smpls wt Rdom Rmovls from t Burr Typ XII Dstrbuto. Jourl of Sttstcl Computto d Smulto. 7; Yrmommd M Pzr H. Mm Estmto of t Prmtr of t Burr Typ XII Dstrbuto. Austrl Jourl of Bsc d Appld Sccs. ; ~64~

A note on Kumaraswamy Fréchet distribution

A note on Kumaraswamy Fréchet distribution AENSI Jourls Austrl Jourl of Bsc d Appld Sccs ISSN:99-878 Jourl hom pg: wwwswcom A ot o Kumrswmy Frécht dstruto Md M E d 2 Ad-Eltw A R Dprtmt of Sttstcs Fculty of Commrc Zgzg Uvrsty Egypt 2 School of Busss

More information

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

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

More information

More Statistics tutorial at 1. Introduction to mathematical Statistics

More Statistics tutorial at   1. Introduction to mathematical Statistics Mor Sttstcs tutorl t wwwdumblttldoctorcom Itroducto to mthmtcl Sttstcs Fl Soluto A Gllup survy portrys US trprurs s " th mvrcks, drmrs, d lors whos rough dgs d ucompromsg d to do t thr ow wy st thm shrp

More information

Statistical properties and applications of a Weibull- Kumaraswamy distribution

Statistical properties and applications of a Weibull- Kumaraswamy distribution Itrtol Jourl of Sttstcs d Appld Mthmtcs 208; 3(6): 8090 ISSN: 2456452 Mths 208; 3(6): 8090 208 Stts & Mths www.mthsjourl.com Rcvd: 09208 Accptd: 20208 Amu M Dprtmt Mths d Sttstcs, Aukr Ttr Al Polytchc,

More information

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING CHPTER 4. FREQUENCY ESTITION ND TRCKING 4.. Itroducto Estmtg mult-frquc susodl sgls burd os hs b th focus of rsrch for qut som tm [68] [58] [46] [64]. ost of th publshd rsrch usd costrd ft mpuls rspos

More information

THE TRANSMUTED GENERALIZED PARETO DISTRIBUTION. STATISTICAL INFERENCE AND SIMULATION RESULTS

THE TRANSMUTED GENERALIZED PARETO DISTRIBUTION. STATISTICAL INFERENCE AND SIMULATION RESULTS Mr l Btr vl Admy Stf Bullt Volum VIII 5 Issu Pulshd y Mr l Btr vl Admy Prss Costt Rom // Th jourl s dd : PROQUST STh Jourls PROQUST grg Jourls PROQUST Illustrt: Thology PROQUST Thology Jourls PROQUST Mltry

More information

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

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

More information

TOTAL LEAST SQUARES ALGORITHMS FOR FITTING 3D STRAIGHT LINES

TOTAL LEAST SQUARES ALGORITHMS FOR FITTING 3D STRAIGHT LINES IJMML 6: (07) 35-44 Mrch 07 ISSN: 394-58 vll t http://sctfcdvcsco DOI: http://ddoorg/0864/jmml_70088 OL LES SQURES LGORIHMS FOR FIING 3D SRIGH LINES Cupg Guo Juhu Pg d Chuto L School of Scc Ch Uvrst of

More information

Reliability of time dependent stress-strength system for various distributions

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

More information

The Research on Position and Orientation Constraint of Rootless Redundant Robots Based on Dynamic Modeling

The Research on Position and Orientation Constraint of Rootless Redundant Robots Based on Dynamic Modeling rd Itrtol orc o chtrocs, Robotcs d Automto IRA 5 h Rsrch o Posto d Ortto ostrt o Rootlss Rdudt Robots Bsd o Dymc odl LI, *, JIA Hyo,b, XI Yzhou,c d ZHA Xp,d Dprtmt o chcl Elctrcl Er, Hb Arculturl Ursty,

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

Exponentiated Weibull-Exponential Distribution with Applications

Exponentiated Weibull-Exponential Distribution with Applications Avlbl t http://pvmudu/m Appl Appl Mth ISSN: 93-9466 Vol, Issu (Dcmb 07), pp 70-75 Applctos d Appld Mthmtcs: A Ittol Joul (AAM) Epottd Wbull-Epotl Dstbuto wth Applctos M Elghy, M Shkl d BM Golm Kb 3 Abstct

More information

3.4 Properties of the Stress Tensor

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

More information

Linear Prediction Analysis of

Linear Prediction Analysis of Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus

More information

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

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

More information

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

OPTIMAL STEP-STRESS PLANS FOR ACCELERATED LIFE TESTING CONSIDERING RELIABILITY/LIFE PREDICTION

OPTIMAL STEP-STRESS PLANS FOR ACCELERATED LIFE TESTING CONSIDERING RELIABILITY/LIFE PREDICTION OPIM P-R PN FOR CCRD IF ING CONIDRING RIBIIY/IF PRDICION Dssrtto Prstd b Chhu to h Dprtmt of Mhl d Idustrl grg prtl fulfllmt of th rqurmt for th dgr of Dotor of Phlosoph Idustrl grg Northstr Uvrst Bosto

More information

A Monotone Process Replacement Model for a Two Unit Cold Standby Repairable System

A Monotone Process Replacement Model for a Two Unit Cold Standby Repairable System Itrtol Jorl of Egrg Rsrch d Dlopmt -ISS: 78-67 p-iss: 78-8 www.jrd.com Volm 7 Iss 8 J 3 PP. 4-49 A Mooto Procss Rplcmt Modl for Two Ut Cold Std Rprl Sstm Dr.B.Vt Rmd Prof.A. Mllrj Rdd M. Bhg Lshm 3 Assstt

More information

Linear Prediction Analysis of Speech Sounds

Linear Prediction Analysis of Speech Sounds Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv

More information

Outline. Outline. Outline. Questions 2010/9/30. Introduction The Multivariate Normal Density and Its Properties

Outline. Outline. Outline. Questions 2010/9/30. Introduction The Multivariate Normal Density and Its Properties 9 Multvrt orml Dstruto Shyh-Kg Jg Drtmt of Eltrl Egrg Grdut Isttut of Commuto Grdut Isttut of tworkg d Multmd Outl Itroduto Th Multvrt orml Dsty d Its Prorts Smlg from Multvrt orml Dstruto d Mmum Lklhood

More information

A Class of Harmonic Meromorphic Functions of Complex Order

A Class of Harmonic Meromorphic Functions of Complex Order Borg Irol Jourl o D Mg Vol 2 No 2 Ju 22 22 A Clss o rmoc Mromorpc Fucos o Complx Ordr R Elrs KG Surm d TV Sudrs Asrc--- T sml work o Clu d Sl-Smll [3] o rmoc mppgs gv rs o suds o suclsss o complx-vlud

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

More information

Accuracy of ADC dynamic parameters measurement. Jiri Brossmann, Petr Cesak, Jaroslav Roztocil

Accuracy of ADC dynamic parameters measurement. Jiri Brossmann, Petr Cesak, Jaroslav Roztocil ccurcy o dymc prmtrs msurmt Jr Brossm Ptr Csk Jroslv Roztocl Czch Tchcl Uvrsty Prgu Fculty o Elctrcl Egrg Tchck CZ-667 Prgu 6 Czch Rpublc Pho: 40-4 35 86 Fx: 40-33 339 9 E-ml: jr.brossm@gml.com cskp@l.cvut.cz

More information

Outline. Outline. Outline. Questions. Multivariate Normal Distribution. Multivariate Normal Distribution

Outline. Outline. Outline. Questions. Multivariate Normal Distribution. Multivariate Normal Distribution Multvrt orml Dstruto hyh-kg Jg Drtmt of Eltrl Egrg Grdut sttut of Commuto Grdut sttut of tworg d Multmd Outl troduto Th Multvrt orml Dsty d ts Prorts mlg from Multvrt orml Dstruto d Mmum Llhood Estmto

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 T Unvrsty o Syny MATH 2009 APH THEOY Tutorl 7 Solutons 2004 1. Lt t sonnt plnr rp sown. Drw ts ul, n t ul o t ul ( ). Sow tt s sonnt plnr rp, tn s onnt. Du tt ( ) s not somorp to. ( ) A onnt rp s on n

More information

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano RIGHT-ANGLE WEAVE Dv mons Mm t look o ts n rlt tt s ptvly p sn y Py Brnkmn Mttlno Dv your mons nto trnls o two or our olors. FCT-SCON0216_BNB66 2012 Klm Pulsn Co. Ts mtrl my not rprou n ny orm wtout prmsson

More information

THE EXPONENTIATED GENERALIZED FLEXIBLE WEIBULL EXTENSION DISTRIBUTION

THE EXPONENTIATED GENERALIZED FLEXIBLE WEIBULL EXTENSION DISTRIBUTION Fudmtl Joul of Mthmtcs d Mthmtcl Sccs Vol. 6 Issu 6 Pgs 75-98 Ths pp s vll ol t http://www.fdt.com/ Pulshd ol Octo 6 THE EXPONENTIATED GENERAIZED FEXIBE WEIBU EXTENSION DISTRIBUTION ABDEFATTAH MUSTAFA

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

Computer Graphics. Viewing & Projections

Computer Graphics. Viewing & Projections Vw & Ovrvw rr : rss r t -vw trsrt: st st, rr w.r.t. r rqurs r rr (rt syst) rt: 2 trsrt st, rt trsrt t 2D rqurs t r y rt rts ss Rr P usuy st try trsrt t wr rts t rs t surs trsrt t r rts u rt w.r.t. vw vu

More information

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees /1/018 W usully no strns y ssnn -lnt os to ll rtrs n t lpt (or mpl, 8-t on n ASCII). Howvr, rnt rtrs our wt rnt rquns, w n sv mmory n ru trnsmttl tm y usn vrl-lnt non. T s to ssn sortr os to rtrs tt our

More information

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

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

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

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

More information

Planar convex hulls (I)

Planar convex hulls (I) Covx Hu Covxty Gv st P o ots 2D, tr ovx u s t sst ovx oyo tt ots ots o P A oyo P s ovx or y, P, t st s try P. Pr ovx us (I) Coutto Gotry [s 3250] Lur To Bowo Co ovx o-ovx 1 2 3 Covx Hu Covx Hu Covx Hu

More information

Unbalanced Panel Data Models

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

More information

ELECTRONIC SUPPLEMENTARY INFORMATION

ELECTRONIC SUPPLEMENTARY INFORMATION Elctronc Supplmntry Mtrl (ESI) or Polymr Cmstry. Ts ournl s T Royl Socty o Cmstry 2015 ELECTRONIC SUPPLEMENTARY INFORMATION Poly(lyln tcont)s An ntrstn clss o polystrs wt proclly loct xo-cn oul ons suscptl

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

CMSC 451: Lecture 4 Bridges and 2-Edge Connectivity Thursday, Sep 7, 2017

CMSC 451: Lecture 4 Bridges and 2-Edge Connectivity Thursday, Sep 7, 2017 Rn: Not ovr n or rns. CMSC 451: Ltr 4 Brs n 2-E Conntvty Trsy, Sp 7, 2017 Hr-Orr Grp Conntvty: (T ollown mtrl ppls only to nrt rps!) Lt G = (V, E) n onnt nrt rp. W otn ssm tt or rps r onnt, t somtms t

More information

JOURNAL OF COLLEGE OF EDUCATION NO

JOURNAL OF COLLEGE OF EDUCATION NO NO.3...... 07 Ivrt S-bst Copproxmto -ormd Spcs Slw Slm bd Dprtmt of Mthmtcs Collg of ducto For Pur scc, Ib l-hthm, Uvrsty of Bghdd slwlbud@yhoo.com l Musddk Dlph Dprtmt of Mthmtcs,Collg of Bsc ducto, Uvrsty

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

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5

More information

In which direction do compass needles always align? Why?

In which direction do compass needles always align? Why? AQA Trloy Unt 6.7 Mntsm n Eltromntsm - Hr 1 Complt t p ll: Mnt or s typ o or n t s stronst t t o t mnt. Tr r two typs o mnt pol: n. Wrt wt woul ppn twn t pols n o t mnt ntrtons low: Drw t mnt l lns on

More information

RISK-NEUTRAL DENSITIES IN ENTROPY THEORY OF STOCK OPTIONS USING LAMBERT FUNCTION AND A NEW APPROACH

RISK-NEUTRAL DENSITIES IN ENTROPY THEORY OF STOCK OPTIONS USING LAMBERT FUNCTION AND A NEW APPROACH H PUBLHNG HOU PROCDNG OF H ROMANAN ACADM rs A OF H ROMANAN ACADM Volm 6 Nmbr /00x pp 0 7 RK-NURAL DN N NROP HOR OF OCK OPON UNG LAMBR FUNCON AND A N APPROACH Vsl PRDA Mhmmd HRAZ Uvrst of Bchrst Fclt of

More information

ME 501A Seminar in Engineering Analysis Page 1

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

More information

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

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

More information

β (cf Khan, 2006). In this model, p independent

β (cf Khan, 2006). In this model, p independent Proc. ICCS-3, Bogor, Idoes December 8-4 Vol. Testg the Equlty of the Two Itercets for the Prllel Regresso Model Bud Prtko d Shhjh Kh Dertmet of Mthemtcs d Nturl Scece Jederl Soedrm Uversty, Purwokerto,

More information

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

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

More information

Irregular Boundary Area Computation. by Quantic Hermite Polynomial

Irregular Boundary Area Computation. by Quantic Hermite Polynomial It. J. Cotmp. Mat. Sccs, Vol. 6,, o., - Irrgular Boudar Ara Computato b Quatc Hrmt Polomal J. Karwa Hama Faraj, H.-S. Faradu Kadr ad A. Jamal Muamad Uvrst of Sulama-Collg of Scc Dpartmt of Matmatcs, Sualma,

More information

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling.

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling. Cptr 4 4 Intrvl Suln Gry Alortms Sls y Kvn Wyn Copyrt 005 Prson-Ason Wsly All rts rsrv Intrvl Suln Intrvl Suln: Gry Alortms Intrvl suln! Jo strts t s n nss t! Two os omptl ty on't ovrlp! Gol: n mxmum sust

More information

IFYFM002 Further Maths Appendix C Formula Booklet

IFYFM002 Further Maths Appendix C Formula Booklet Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,

More information

Lecture 20: Minimum Spanning Trees (CLRS 23)

Lecture 20: Minimum Spanning Trees (CLRS 23) Ltur 0: Mnmum Spnnn Trs (CLRS 3) Jun, 00 Grps Lst tm w n (wt) rps (unrt/rt) n ntrou s rp voulry (vrtx,, r, pt, onnt omponnts,... ) W lso suss jny lst n jny mtrx rprsntton W wll us jny lst rprsntton unlss

More information

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045)

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045) FACTA UNIVERSITATIS Srs: Mcacs Automatc Cotrol ad Rootcs Vol 4 N o 6 4 pp 33-39 A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 59645 Prdrag M Raovć Momr S Staovć Slađaa D Marovć 3 Dpartmt

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

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

More information

n r t d n :4 T P bl D n, l d t z d th tr t. r pd l

n r t d n :4 T P bl D n, l d t z d   th tr t. r pd l n r t d n 20 20 :4 T P bl D n, l d t z d http:.h th tr t. r pd l 2 0 x pt n f t v t, f f d, b th n nd th P r n h h, th r h v n t b n p d f r nt r. Th t v v d pr n, h v r, p n th pl v t r, d b p t r b R

More information

Influence of Pareto optimality on the Maximum Entropy Methods

Influence of Pareto optimality on the Maximum Entropy Methods Ifluc of Prto optmlt o th Mmum Etrop Mthods Srhr Pddvrpu Gujjlpud Vkt S Sul d Rghurm S School of Mchcl Egrg SASTRA Uvrst Thjvur Tml du-60 Corrspodg uthor: srhr@mch.sstr.du gvssul997@sstr.c. Astrct. Glrk

More information

Th n nt T p n n th V ll f x Th r h l l r r h nd xpl r t n rr d nt ff t b Pr f r ll N v n d r n th r 8 l t p t, n z n l n n th n rth t rn p rt n f th v

Th n nt T p n n th V ll f x Th r h l l r r h nd xpl r t n rr d nt ff t b Pr f r ll N v n d r n th r 8 l t p t, n z n l n n th n rth t rn p rt n f th v Th n nt T p n n th V ll f x Th r h l l r r h nd xpl r t n rr d nt ff t b Pr f r ll N v n d r n th r 8 l t p t, n z n l n n th n rth t rn p rt n f th v ll f x, h v nd d pr v n t fr tf l t th f nt r n r

More information

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

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

More information

8(4 m0) ( θ ) ( ) Solutions for HW 8. Chapter 25. Conceptual Questions

8(4 m0) ( θ ) ( ) Solutions for HW 8. Chapter 25. Conceptual Questions Solutios for HW 8 Captr 5 Cocptual Qustios 5.. θ dcrass. As t crystal is coprssd, t spacig d btw t plas of atos dcrass. For t first ordr diffractio =. T Bragg coditio is = d so as d dcrass, ust icras for

More information

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

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

More information

I N A C O M P L E X W O R L D

I N A C O M P L E X W O R L D IS L A M I C E C O N O M I C S I N A C O M P L E X W O R L D E x p l o r a t i o n s i n A g-b eanste d S i m u l a t i o n S a m i A l-s u w a i l e m 1 4 2 9 H 2 0 0 8 I s l a m i c D e v e l o p m e

More information

Almost unbiased exponential estimator for the finite population mean

Almost unbiased exponential estimator for the finite population mean Almos ubasd poal smaor for f populao ma Rajs Sg, Pakaj aua, ad rmala Saa, Scool of Sascs, DAVV, Idor (M.P., Ida (rsgsa@aoo.com Flor Smaradac ar of Dparm of Mamacs, Uvrs of Mco, Gallup, USA (smarad@um.du

More information

On Hamiltonian Tetrahedralizations Of Convex Polyhedra

On Hamiltonian Tetrahedralizations Of Convex Polyhedra O Ht Ttrrzts O Cvx Pyr Frs C 1 Q-Hu D 2 C A W 3 1 Dprtt Cputr S T Uvrsty H K, H K, C. E: @s.u. 2 R & TV Trsss Ctr, Hu, C. E: q@163.t 3 Dprtt Cputr S, Mr Uvrsty Nwu St. J s, Nwu, C A1B 35. E: w@r.s.u. Astrt

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

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

More information

SYSTEMS OF LINEAR EQUATIONS

SYSTEMS OF LINEAR EQUATIONS SYSES OF INER EQUIONS Itroducto Emto thods Dcomposto thods tr Ivrs d Dtrmt Errors, Rsdus d Codto Numr Itrto thods Icompt d Rdudt Systms Chptr Systms of r Equtos /. Itroducto h systm of r qutos s formd

More information

Properties of Demand

Properties of Demand AGEC 5733 LECTURE NOTES DR. SHIDA HENNEBERR ROERTIES OF DEMAND AGEC 5733 cl ot /4 /9 d / rort of Dmd Grl rort of Dmd:. El Arto. Homoty 3. Courot d 4. Symmtry Mtr of Eltct M α α M M α L LL M M wr dtur Q

More information

Dual to Separate Ratio Type Exponential Estimator in Post-Stratification

Dual to Separate Ratio Type Exponential Estimator in Post-Stratification J tt Appl Pro 3, o 3, 5-3 (0 5 Jourl of ttistics Applictios & Probbilit A Itrtiol Jourl ttp://ddoiorg/0785/jsp/03033 Dul to prt Rtio Tp Epotil Estimtor i Post-trtifictio Hill A o d Rjs Tilor cool of tudis

More information

CMPS 2200 Fall Graphs. Carola Wenk. Slides courtesy of Charles Leiserson with changes and additions by Carola Wenk

CMPS 2200 Fall Graphs. Carola Wenk. Slides courtesy of Charles Leiserson with changes and additions by Carola Wenk CMPS 2200 Fll 2017 Grps Crol Wnk Sls ourtsy o Crls Lsrson wt ns n tons y Crol Wnk 10/23/17 CMPS 2200 Intro. to Alortms 1 Grps Dnton. A rt rp (rp) G = (V, E) s n orr pr onsstn o st V o vrts (snulr: vrtx),

More information

A Measure of Inaccuracy between Two Fuzzy Sets

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

More information

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

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

More information

Special Curves of 4D Galilean Space

Special Curves of 4D Galilean Space Irol Jourl of Mhml Egrg d S ISSN : 77-698 Volum Issu Mrh hp://www.jms.om/ hps://ss.googl.om/s/jmsjourl/ Spl Curvs of D ll Sp Mhm Bkş Mhmu Ergü Alpr Osm Öğrmş Fır Uvrsy Fuly of S Dprm of Mhms 9 Elzığ Türky

More information

Priority Search Trees - Part I

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

More information

Bayesian Approach to Generalized Normal Distribution under Non- Informative and Informative Priors

Bayesian Approach to Generalized Normal Distribution under Non- Informative and Informative Priors I.J. Mtmtc Sccs d Comutg 8 9- Pusd O Novm 8 MCS (tt://www.mcs-ss.t) DOI:.585/jmsc.8.. Av o t tt://www.mcs-ss.t/jmsc Bys Aoc to Gd Nom Dstuto ud No- Ifomtv d Ifomtv Pos Sm Nqs * S.P.Amd Aqu Amd Dtmt of

More information

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd n r t d n 20 20 0 : 0 T P bl D n, l d t z d http:.h th tr t. r pd l 4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n,

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Efficient Computations for Evaluating Extended Stochastic Petri Nets using Algebraic Operations

Efficient Computations for Evaluating Extended Stochastic Petri Nets using Algebraic Operations Itrtol Jourl of Cotrol Automto d Sytm Vol No 4 Dcmbr 4 Effct Computto for Evlutg Extdd Stochtc Ptr Nt ug Algbrc Oprto Dog-Sug Km Hog-Ju Moo J-Hyog Bh Woo Hyu Kwo d Zygmut J H Abtrct: Th ppr prt ffct mthod

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

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

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

More information

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e)

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e) POW CSE 36: Dt Struturs Top #10 T Dynm (Equvln) Duo: Unon-y-Sz & Pt Comprsson Wk!! Luk MDowll Summr Qurtr 003 M! ZING Wt s Goo Mz? Mz Construton lortm Gvn: ollton o rooms V Conntons twn t rooms (ntlly

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

Quantum Harmonic Oscillator

Quantum Harmonic Oscillator Quu roc Oscllor Quu roc Oscllor 6 Quu Mccs Prof. Y. F. C Quu roc Oscllor Quu roc Oscllor D S..O.:lr rsorg forc F k, k s forc cos & prbolc pol. V k A prcl oscllg roc pol roc pol s u po of sbly sys 6 Quu

More information

Multiple-Choice Test Runge-Kutta 4 th Order Method Ordinary Differential Equations COMPLETE SOLUTION SET

Multiple-Choice Test Runge-Kutta 4 th Order Method Ordinary Differential Equations COMPLETE SOLUTION SET Multpl-Co Tst Rung-Kutta t Ordr Mtod Ordnar Drntal Equatons COMPLETE SOLUTION SET. To solv t ordnar drntal quaton sn ( Rung-Kutta t ordr mtod ou nd to rwrt t quaton as (A sn ( (B ( sn ( (C os ( (D sn (

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

,. *â â > V>V. â ND * 828.

,. *â â > V>V. â ND * 828. BL D,. *â â > V>V Z V L. XX. J N R â J N, 828. LL BL D, D NB R H â ND T. D LL, TR ND, L ND N. * 828. n r t d n 20 2 2 0 : 0 T http: hdl.h ndl.n t 202 dp. 0 02802 68 Th N : l nd r.. N > R, L X. Fn r f,

More information

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2014 Lecture 20: Transition State Theory. ERD: 25.14

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2014 Lecture 20: Transition State Theory. ERD: 25.14 Univrsity of Wasinton Dpartmnt of Cmistry Cmistry 453 Wintr Quartr 04 Lctur 0: Transition Stat Tory. ERD: 5.4. Transition Stat Tory Transition Stat Tory (TST) or ctivatd Complx Tory (CT) is a raction mcanism

More information

On the Existence and uniqueness for solution of system Fractional Differential Equations

On the Existence and uniqueness for solution of system Fractional Differential Equations OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o

More information

Linear Algebra Existence of the determinant. Expansion according to a row.

Linear Algebra Existence of the determinant. Expansion according to a row. Lir Algbr 2270 1 Existc of th dtrmit. Expsio ccordig to row. W dfi th dtrmit for 1 1 mtrics s dt([]) = (1) It is sy chck tht it stisfis D1)-D3). For y othr w dfi th dtrmit s follows. Assumig th dtrmit

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

Re-synthesis for Delay Variation Tolerance

Re-synthesis for Delay Variation Tolerance 49.1 R-sytss or Dly Vrto Tolr S-C C Dprtmt o CS Ntol Ts Hu Uvrsty Hsu, Tw s@s.tu.u.tw C-To Hs Dprtmt o CS Ntol Ts Hu Uvrsty Hsu, Tw s@tu.s.tu.u.tw K-C Wu Dprtmt o CS Ntol Ts Hu Uvrsty Hsu, Tw Alx@tu.s.tu.u.tw

More information

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F

More information

Quantitative Genomics and Genetics BTRY 4830/6830; PBSB

Quantitative Genomics and Genetics BTRY 4830/6830; PBSB Quntttv Gnomcs n Gntcs BTRY 4830/6830; BSB.520.0 Lctur8: Logstc rgrsson II Json Mzy jgm45@cornll.u Aprl 0, 208 (T 8:40-9:55 Announcmnts Grng (homworks n mtrm rojct wll b ssgn NEXT WEEK (!! Schul for th

More information

D t r l f r th n t d t t pr p r d b th t ff f th l t tt n N tr t n nd H n N d, n t d t t n t. n t d t t. h n t n :.. vt. Pr nt. ff.,. http://hdl.handle.net/2027/uiug.30112023368936 P bl D n, l d t z d

More information

Let's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = =

Let's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = = L's rvs codol rol whr h v M s rssd rs o h rdo vrl. L { M } rrr v such h { M } Assu. { } { A M} { A { } } M < { } { } A u { } { } { A} { A} ( A) ( A) { A} A A { A } hs llows us o cosdr h cs wh M { } [ (

More information

Odd Generalized Exponential Flexible Weibull Extension Distribution

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

More information

New Error Model of Entropy Encoding for Image Compression

New Error Model of Entropy Encoding for Image Compression Wb St: www.jttcs.org Eml: dtor@jttcs.org, dtorjttcs@gml.com Volum, Issu, Mrch Aprl 03 ISSN 78-6856 Nw Error Modl of Etropy Ecodg for Img Comprsso Moht Mshr, Rjsh Tjw, Assstt Profssor Ary Collg of Egrg

More information

NHPP and S-Shaped Models for Testing the Software Failure Process

NHPP and S-Shaped Models for Testing the Software Failure Process Irol Jourl of Ls Trds Copug (E-ISSN: 45-5364 8 Volu, Issu, Dcr NHPP d S-Shpd Modls for Tsg h Sofwr Flur Procss Dr. Kr Arr Asss Profssor K.J. Soy Isu of Mg Suds & Rsrch Vdy Ngr Vdy Vhr Mu. Id. dshuh_3@yhoo.co/rrr@ssr.soy.du

More information

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

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

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

Position Control of 2-Link SCARA Robot by using Internal Model Control

Position Control of 2-Link SCARA Robot by using Internal Model Control Mmors of th Faculty of Er, Okayama Uvrsty, Vol, pp 9-, Jauary 9 Posto Cotrol of -Lk SCARA Robot by us Itral Modl Cotrol Shya AKAMASU Dvso of Elctroc ad Iformato Systm Er Graduat School of Natural Scc ad

More information

828.^ 2 F r, Br n, nd t h. n, v n lth h th n l nd h d n r d t n v l l n th f v r x t p th l ft. n ll n n n f lt ll th t p n nt r f d pp nt nt nd, th t

828.^ 2 F r, Br n, nd t h. n, v n lth h th n l nd h d n r d t n v l l n th f v r x t p th l ft. n ll n n n f lt ll th t p n nt r f d pp nt nt nd, th t 2Â F b. Th h ph rd l nd r. l X. TH H PH RD L ND R. L X. F r, Br n, nd t h. B th ttr h ph rd. n th l f p t r l l nd, t t d t, n n t n, nt r rl r th n th n r l t f th f th th r l, nd d r b t t f nn r r pr

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

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th n r t d n 20 0 : T P bl D n, l d t z d http:.h th tr t. r pd l 46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l

More information