Inference on Stress-Strength Reliability for Weighted Weibull Distribution

Size: px
Start display at page:

Download "Inference on Stress-Strength Reliability for Weighted Weibull Distribution"

Transcription

1 Arca Joural of Mathatcs a Statstcs 03, 3(4: 0-6 DOI: 0.593/j.ajs Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Hay M. Sal Dpartt of Statstcs, Faculty of Corc, Al-Azhr Uvrsty, Egypt & Qass Uvrsty, Couty Collg Buraah, Sau Araba Abstract Ths papr als wth th stato of th rlablty R p( Y < X = wh X a Y ar pt varabls strbut as Wght Wbull Dstrbuto. Dffrt thos for statg th rlablty ar obta such as Mau Lklhoo Estators, Last Squar Estators a Baysa Estators whch ar bas o o-foratv a foratv pror strbutos. A coparso of th stats obta s prfor as wll. Fally a urcal vstgato s carr out to stuy th proprts of th w stators. Kywors Rlablty, Strss-Strgth, Mau Lklhoo Estator; Baysa Estator, Wght Wbull Dstrbutos. Itroucto It has bco a fact that provg th qualty of proucts ps aly o th o gog a for ths proucts. I ato, stayg th arkt has bco also assocat wth th rlablty of such proucts. For ths rasos, copas to t spcfc asur to copt worl w arkts such as, ts ablty to hgh qualty, copttv prcs, a rlabl goos o t. I ths rgar s, aufacturs us foratv asssst for thr proucts to achv rlablty whch s closly rlat to urablty, accssblty a survval. Falur ca b cos r a rao varabl sc t s so ffcult to tll prcsly wh a spcfc prouct wll fal ur us coto. Ur oral coto, asur of rlablty for vc bcos too ffcult a t rqurs vry log t. So, acclrat lf tstg ucs falurs a th falur ata at th acclrat cotos ar us to stat th rlablty at oral opratg cotos wh th rlablty of a copot s "hgh" a falur ata of th copot ay ot b attaabl urg ts pct lf. Th probl of R = p Y < X arss th statg th rlab lty ( stuato of chacal rlablty of a syst wth strgth X a strss Y, a R s a tr of syst rlablty. Th syst fals f a oly f, at ay t th strss cs th strgth. May authors hav asur ffrt chocs for strss a strgth strbutos. Johso (988[0] suarz * Corrspog author:.haysal@yahoo.co (Hay M. Sal Publsh ol at ajs Copyrght 03 Sctfc & Acac Publshg. All Rghts Rsrv so of ths chocs. Awa & Gharraf (986[5] cosr th cas wh X a Y ar pt a hav Burr Typ XII rao varabls. Thy obta a au lklhoo stator, u varac ubas stator, a a Baysa stator for R. Thy us a urcal procur for valuatg thr Baysa stator. Ala & Rooh (00[3] stu strss a strgth havg potal strbuto but (003[4] hav stu th R = p Y < X a ffrt way. Thy hav st probl of ( up th rqur paratrc valus of th assu strbutos as a rplact for fg P (Y<X for a gv st of strbuto, For th a, thy assu potal strgth a potal strss. Kotz t al. (003[] prst a rvw of all thos a rsults o th strss-strgth ol th last four cas. Ab-Elfattah & Maouh (004[] obta th thr stators of R for Loa strbuto wth kow scal paratr, ths stators ar au lklhoo stator, uforly Mu varac ubas stato (UMVUE a Bays stator. Mokhls (005[4] stu th cas wh X a Y ar pt rao varabls Burr typ III, h obta th au lklhoo stator (MLE, Mu varac ubas stato (UMVUE a Baysa stats of R, h copar btw th stators by usg Mot Carlo sulato. Latr Kha & Isla (007[] alt wth probl for powr fucto strbuto. Th Wbull strbuto s o of th ost wly us strbutos th rlablty a survval stus. Baklz (0[7] cosr th strss-strgth rlablty bas o rcor valus fro th Wbull strbuto. H obta th Bays stator bas o squar rror loss a th au lklhoo stator of th rlablty R. Akbar t al (0 [] focus o th frc for th strss- strgth

2 Arca Joural of Mathatcs a Statstcs 03, 3(4: 0-6 rlablty R wh X a Y ar two pt Wbull strbutos wth th sa shap paratr, but havg th ffrt scal paratrs. Thy obta Th au lklhoo a th approat au lklhoo stators of R. Azzal (985[6] suggst a tho of obtag wght strbutos fro ptly tcally strbut rao varabls. H us th sty fucto of o rao varabl a th strbuto fucto of th othr rao varabl as follows: FX ( = fy ( FY ( (. P( X > X Rctly, Gupta & Kuu, (009[9] prst a w class of wght potal strbutos. Makhoo (0[3] stu th stato of Strss-Strgth rlablty wh X a Y ar two wght potal strbutos wth ffrt paratrs. H obta th MLE of R bas o o spl trato procur, a h carr out Baysa stators of paratrs wth ral ata. Sa a t al. (00[5] propos Th wght Wbull ol bas o a a of Azzal (985[6]. Thy stu basc proprts of th strbuto clug ots, gratg fucto, hazar rat fucto a stato of paratrs. Th proposg ol whch Saa t all (00[5] to valuat th paratrs of Wght Wbull strbuto s cosr hr. Th, th cuulatv strbuto fucto s: + FX ( { p( } { p( ( = + } + (. ;,, λ, > 0 a th probablty sty fucto s: whr + fx (. ;,, λ, > 0 hc, λ ar shap paratrs a s scal paratr. Now, lt λ = as th sa us ar at Sa a t all (00[5]. Ths papr cossts of thr sctos corrspog to sctos, 3, a 4, rspctvly. scto provs a approach for schg Strss-Strss rlablty syst. scto 3, scusss th au lklhoo Estators, Last Squar Estators a Baysa stators of th rlablty R. Fally a u rcal vstgato wll b carr out to stuy th proprts of th w stators. ( = λ p( λ p ( λ. Syst Rlablty ( + ( + ( + + = + Lt X b th strgth of a syst a Y b th strss actg o t. X a Y ar th rao varabls fro Wght Wbull strbutos wth paratrs (, a (, rspctvly. Thrfor, th probablty sty fuctos of X a Y ar, rspctvly: ( + f X = p( p( (. ;,, > 0 ( + f Y y = p( p( (. ;,, y > 0 ar shap paratrs a s scal whr a paratr. Th, th rlablty fucto R s: R = P( y < = FY ( f ( 0 + { p( } { p( ( = + } p( p( = c + ( ( + + c + + = + c a c. (3. 3. Pot Estator of th Rlablty R 3.. Ma u Lklhoo Est ator

3 Hay M. Sal: Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Lt X, X,, X Y, Y,, Y a strbutos wth paratrs (, a (, th abov sapls ar rspctvly gv as: b th two pt rao sapls tak fro th Wght Wbull rspctvly, th, th lklhoo a log-lklhoo fucto bas o L = y = = y ( (, = = ( ( ( = l + l + l + l + + l = = ( y + + y+ + = = = = Th rvatvs of (, ;, y ar, rspctvly: l l l l. l wth rspct to a = + 0, ˆˆ = + = y y = + 0, ˆˆ = (4.3 + y = l l y l l y y + = + + y = (5.3 y = = = = Ufortuatly, thr s o a valu for (,, (3.3, (4.3 a (5.3, so wth that, Nwto Raphso tho a tratv approach to solv ths quatos urcal aalyss s cosr. It s a tratv tho for solvg quatos f ( t = 0 whr f (t s assu to hav a cotuous rvatv f (t. Gv a fucto f (t a ts rvatv f (t, a frst guss t 0 s tal. Th, a approato of t ( s f t0 t0 a a approato of t f ( t0 f ( t th s t a so o for ubr of tratos r or f tr+ tr τ whr t s th r r stat. f ( t Now, rplac tˆ whch gvs fro trato wth th paratr t aftr trato procss for statg th paratr t quatos (3.3, (4.3 a (5.3. Sc, Mau Lklhoo Estators ar o ot chag, So t bcos: Rˆ MLE = c + c + (6.3 ˆˆˆˆˆˆˆˆ ( ˆˆˆˆˆˆ + ( + ( + ( + ( + + whr ˆ ˆ + ˆ + c = a c ˆ ˆ = ˆ Last S quar Est ators Suppos that (. s a lar rlato btw th two varabls a tak th logarth of th two ss as follows: y (.3 (.3 (3.3

4 Arca Joural of Mathatcs a Statstcs 03, 3(4: ( l ( l ( l ( l F + = + + +, So, f ths fucto s z wth rspct to (, w up wth th Last Squar Estators. Ths tho s kow as Last Squar tho (or tals, s Flah t al (0[8]. Now, usg th a rak tho, w ca stat F ( fro ths rlato ar th rak falur ts. + ( = + (7.3,,, F, whr Lt y = l(, so, (7.3 bcos straght l quato. Thrfor, th last squar stators of fro zg th followg quato: a co Q ( ( ( (, y l l l + = = Q, wth rspct to a, rspctvly, ar: Q (, ( + = y l ( + + l ( l ( = + ( + ( + ( ( ( = + + = + +, + ( + ( + = Q (, ( ( ( l l l y + = + + = + ( ( ( ( l l + + = =. ( + + = Q, wth rspct to s: Q (, ( ( ( l l l y y y + = + + = + ( + y ( + y y ( + = + = ( y + y + = (8.3 Th frst partal rvatvs of ( Slarly, th frst partal rvatvs of ( (9.3 (0.3 (.3

5 4 Hay M. Sal: Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto If both ( Q,, ( Q, a ( Q, qual zro, th, th Last Squar Estators of, a wll rsult atly. Howvr, ths procss s too ffcult to b o wthout urcal soluto. So, w aapt th prvous tchqu of, Nwto Raphso tho Bays Estator of Rlablty R Lt X, X,, X Y, Y,, Y a strbutos wth paratrs (, a (, wth paratrs ( λ, θ a ( τ whr ( λ θ, ε, τ b two pt rao sapls, raw fro th Wght Wbull rspctvly, a assocat gaa pror strbutos for ε, ar ploy rspctvly, so that th pror strbuto for a ar: a π ( λ θ, > 0 (.3 π ε τ, > 0 (3.3 (, ar pt. A th jot sty fucto for ata a th paratrs a s: ( ; (, θ τ ata λ + ε = + = = Th, th postror sty fucto for a bas o ata s: ( ata;,, ata = ( 0 0 (4.3 ( ata;, Now, by usg Postror Mo Mtho to obta th Baysa stators of paratrs as follows: Th log postror strbuto for th sapl prors ( λ, θ a ( τ ε, rspctvly s: X, X,, X (5.3 tak fro Wght Wbull strbuto wth Gaa ( ata ( ( λ ( ( ε ( l, l + + l + + l ( l ( l ( ( θ τ l C = = = whr C os ot pt o th paratrs, a th paratrs λ θ, ε Th frst rvatvs of l(, ;, y wth rspct to l (, λ Lt h Th, (, a τ ar f. a ar, rspctvly: (6.3 ata = + θ + (7.3 + = g.l( l, ata + ε = + g λ = + θ + + g.l( = l l ( τ + (8.3 = = = + ε, a h= + l l ( τ +. ( + = g = = = h λ =. g (9.3 h + ε = ( l ( + g g = = (0.3

6 Arca Joural of Mathatcs a Statstcs 03, 3(4: h h = = g g = (.3 whr ( b.l = g =. So, th Hssa atr of th rvatvs I s : l ( (, ata l, ata I = ( ( (.3 l, ata l, ata Th objct of ths work s stat ˆ δ = (, urcally to f ˆ δ ˆ + = δ + I ϕ whr ϕ = [ h h ]. Soluto ca b covrg usg Nwto Raphso algorth to stat, whch ar rplac by ˆ ˆ, gla fro trato. 4. Sulato Stuy Ta bl (. Wh R MLE R BN-IF R B-IF MSE MLE MSE BN-IF MSE B-IF Ta bl (. Wh R MLE R BN-IF R B-IF MSE MLE MSE BN-IF MSE B-IF

7 6 Hay M. Sal: Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Th coputr progras MathCAD (00 s us to obta urcal llustrato for th last thortcal rsults. A coparso btw th thr stators, MLE, Bays bas o o-foratv stator a Bays bas o foratv stator s prfor. 00 sapls grat fro Wght Wbull strbutos wth paratrs (, a (, ar us, rspctvly, a ffrt valus of, a wth varous szs ( 5, 0, 0, 30 for both a tabl. Also, varous szs ( 5, 50, 75, 00 for th tabl. Th th as of ths rplcats ar calculat to obta au lklhoo stator of R (R MLE, Bays stator bas o o-foratv (R BN-IF a Bays stator bas o foratv R B-IF. Th a squar rror of R MLE, R BN-IF a R B-IF ar coput as wll. Fro Tabl ( a (, ot that th a squar rror of R B-IF s sallr tha ach of th a squar rror of R MLE a R BN-IF f th sapl szs, ar s all or larg. But, tabl (, wh th sapl szs ar larg th a squar rror of all stators ar lss tha th othr. For apl, wh th sapl szs = 5 a = 0, w f that th a squar rrors of R MLE a R BN-IF a R B-IF ar (0.055, a 0.00 rspctvly, whl, thy ar (0.0, 0.09 a wh th sapl szs ar = 5 a = 75. Ths bhavor appls o th rst of paratrs,,, λ, θ, ε a τ as wll. Also, th thr stators R B-IF, R MLE a R BN-IF ar cras wth crasg th sapl sz a tak th sapl sz s f, a thy ar cras wth crasg th sapl sz a tak th sapl sz s f. O th othr ha, thy ar cras wth crasg both th sapl szs a. 5. Coclusos I ths papr, th stato probl of th rlablty of a syst R strss-strgth ol wh X a Y ar pt varabls strbut as Wght Wbull Dstrbuto s cosr. Mau Lklhoo Estators, Last Squar Estators a Baysa Estators bas o o-foratv a foratv pror strbutos wr scuss va urcal rsults. REFERENCES [] Ab-Elfattah, A. M., Maouh, R. M. (004. Estato of P(Y < X Loa cas. Th 39 th Aual Cofrc o Statstcs, Coputr Scc a Oprato Rsarch, ISSR, Caro Uvrsty, Egypt, part, [] Akbar A., Rza V. a Mohaa Z. (0. Strss-strgth rlablty of Wbull strbuto bas o progrssvly csor sapls3sort 35 (, [3] Ala, S.N. a Rooh (00: O augtg potal strgth-rlablty, IAPQR Trasactos. 7, -7. [4] Ala, S.N. a Rooh (003: O facg a potal strss a strgth havg powr fucto strbuto. Algarh J. Statstc. 3, [5] Awa, A.M., Gharraf, M.K. (986 Estato of P(Y < X th Burr cas: a coparatv stuy. Cou. Statstc. - Sul. Cop., 5, [6] Azzal, A. (985. A class of strbutos whch clu th oral os, Sca. J. Stat., Vol., [7] Baklz A. (0 Ifrc o ( Y X pr < th Two-Paratr Wbull Mol Bas o Rcors. Itratoal Scholarly Rsarch Ntwork. Volu 0. o:0.540/0/636. [8] Flah, A., Elsalloukh, H., M, a E. a Mlaova, M. (0. Th Epotat Ivrt Wbull Dstrbuto. Appl. Math. If. Sc. 6, No., [9] Gupta, R. D. & Kuu, D. (009. A w class of wght potal strbutos, Statstcs, 43(6, [0] Johso, R.A. (988 Strss-strgth Mols for Rlablty. I Habook of Statstcs. E. Krshaah, P.R. a Rao, C.R., Vol. 7, Elsvr, North Holla, [] Kha, M.A. a Isla, H.M. (007: O facg Raylgh strss wth strgth havg powr fucto strbuto. J. Appl. Statst. Sc. 6 pp [] Kotz, S., Lulsk, Y. a Psky, M. (003. Th Strss-Strgth Mol a Its Gralzatos. Nw Jrsy: Worl Sctfc, Ic. [3] Makhoo, I. (0. Estato of R p( Y < X = for Wght Epotal Dstrbuto. J. Appl Sc. ( [4] Mokhls, N. A. (005 Rlablty of a Strss-Strgth Mol wth Burr Typ III Dstrbutos. Co. Statst. - Thory Mth., 34, [5] Saa, S., Muhaa, Q. & Na, S. (00. A class of Wght Wbull Dstrbuto. Pak. j. stat. opr. rs. Vol. VI No. 00 pp Elctroc copy avalabl at: co/abstract=77489.

ONLY AVAILABLE IN ELECTRONIC FORM

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

More information

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION

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

More information

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

Estimation of Population Variance Using a Generalized Double Sampling Estimator

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

More information

Correlation in tree The (ferromagnetic) Ising model

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

More information

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

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

More information

Power Spectrum Estimation of Stochastic Stationary Signals

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

More information

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

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

More information

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

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

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

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

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

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

More information

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

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

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

More information

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

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

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

More information

Introduction to logistic regression

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

More information

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

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

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

More information

Nuclear Chemistry -- ANSWERS

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

More information

A study of stochastic programming having some continuous random variables

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

More information

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

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

More information

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

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes 3.3 Physcal Orato of os Jucto Ur vrs-bas Cotos rft Currt S : ato to th ffuso Currt comot u to majorty carrr ffuso, caus by thrmally grat morty carrrs, thr ar two currt comots lctros mov by rft from to

More information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data

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

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh Worl cay of cc Er a choloy Itratoal Joural of oputr a Iato Er Vol:8 o:7 4 Optal s of wo-hal Rcursv aralllora Quaratur rror Fltr Baks Ju-o L Y-L hh Itratoal cc Ix oputr a Iato Er Vol:8 o:7 4 wast.orublcato99989

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Independent Domination in Line Graphs

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

More information

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

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 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

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

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

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

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

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

More information

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

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

Homework 1: Solutions

Homework 1: Solutions Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc

More information

Lecture 1: Empirical economic relations

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

More information

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

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

More information

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

Aotomorphic Functions And Fermat s Last Theorem(4)

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

More information

Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution

Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution World Acade of Scece, Egeerg ad Techolog Iteratoal Joural of Matheatcal ad Coputatoal Sceces Vol:4, No:, Estato of R P [Y < X] for Two-paraeter Burr Tpe XII Dstruto H.Paah, S.Asad Iteratoal Scece Ide,

More information

Extension of Two-Dimensional Discrete Random Variables Conditional Distribution

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

More information

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst

More information

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

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

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

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

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

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

More information

STK4011 and STK9011 Autumn 2016

STK4011 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 information

In 1991 Fermat s Last Theorem Has Been Proved

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

More information

Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables. Arzdar Kiraci. Siirt University ABSTRACT

Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables. Arzdar Kiraci. Siirt University ABSTRACT Itratoal Ecootrc Rw IER Cofrato, Corrcto a Iprot for Outlr Valato usg Du Varabls Arzar Krac Srt Urst ABSTRACT Du arabls ca b us to tct, alat a asur th pact of outlrs ata. Ths papr uss a ol to aluat th

More information

A Simple Representation of the Weighted Non-Central Chi-Square Distribution

A Simple Representation of the Weighted Non-Central Chi-Square Distribution SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

Introduction to logistic regression

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

More information

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

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

More information

In the name of Allah Proton Electromagnetic Form Factors

In the name of Allah Proton Electromagnetic Form Factors I th a of Allah Poto Elctoagtc o actos By : Maj Hazav Pof A.A.Rajab Shahoo Uvsty of Tchology Atoc o acto: W cos th tactos of lcto bas wth atos assu to b th gou stats. Th ct lcto ay gt scatt lastcally wth

More information

The Beta Inverted Exponential Distribution: Properties and Applications

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

More information

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

Numerical Method: Finite difference scheme

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

More information

Generalizedextended Weibull Power Series Family of Distributions

Generalizedextended Weibull Power Series Family of Distributions Arca Rvw o Mathatcs ad Statstcs Dcbr 205 Vol. 3 No. 2 pp. 53-68 SSN: 2374-2348 (Prt 2374-2356 (Ol Copyrght Th Author(s. All Rghts Rsrvd. Publshd by Arca Rsarch sttut or Polcy Dvlopt DO: 0.5640/ars.v32a8

More information

Almost all Cayley Graphs Are Hamiltonian

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

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

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

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

More information

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

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

More information

Using Nonlinear Filter for Adaptive Blind Channel Equalization

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

More information

Bayesian Estimation of the Logormal Distrbution Mean Using Ranked SET Sampling

Bayesian Estimation of the Logormal Distrbution Mean Using Ranked SET Sampling Basrah ournal o Sn Vol.5-7 Basan Estaton o th ooral Dstrbuton Man Usn Ran SET Sapln R.. h bstrat Bas staton or onoral an usn Ran St Sapln s onsr n ths papr an opar to that usn Spl Rano Sapln. It as sho

More information

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

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

More information

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors Dfnton Th nvr of an n by n atrx A an n by n atrx B whr, Not: nar Algbra Matrx Invron atrc on t hav an nvr. If a atrx ha an nvr, thn t call. Proprt of Matrx Invr. If A an nvrtbl atrx thn t nvr unqu.. (A

More information

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

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

More information

minimize c'x subject to subject to subject to

minimize c'x subject to subject to subject to z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

1 Solution to Problem 6.40

1 Solution to Problem 6.40 1 Soluto to Problem 6.40 (a We wll wrte T τ (X 1,...,X where the X s are..d. wth PDF f(x µ, σ 1 ( x µ σ g, σ where the locato parameter µ s ay real umber ad the scale parameter σ s > 0. Lettg Z X µ σ we

More information

COMPUTATION OF TOPOLOGICAL INDICES OF INTERSECTION GRAPHS AND CONCENTRIC WHEELS GRAPH

COMPUTATION OF TOPOLOGICAL INDICES OF INTERSECTION GRAPHS AND CONCENTRIC WHEELS GRAPH TH PUBLISHIN HOUS PROCDINS OF TH ROMANIAN ACADMY Srs A OF TH ROMANIAN ACADMY ol Nbr /00x pp 8 90 COMPUTATION OF TOPOLOICAL INDICS OF INTRSCTION RAPHS AND CONCNTRIC WHLS RAPH Mh ALAIYAN Rasol MOJARAD Jafar

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

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

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

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

More information

International Journal of Mathematical Archive-6(5), 2015, Available online through ISSN

International Journal of Mathematical Archive-6(5), 2015, Available online through  ISSN Itratoal Joural of Mathmatal Arhv-6), 0, 07- Avalabl ol through wwwjmafo ISSN 9 06 ON THE LINE-CUT TRANSFORMATION RAPHS B BASAVANAOUD*, VEENA R DESAI Dartmt of Mathmats, Karatak Uvrsty, Dharwad - 80 003,

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

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

More information

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

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

More information

BAYESAIN ESTIMATION OF SIZE BIASED CLASSICAL GAMMA DISTRIBUTION

BAYESAIN ESTIMATION OF SIZE BIASED CLASSICAL GAMMA DISTRIBUTION Joural o lablty a Statstal Stus; ISSN Prt: 974-84 Ol:9-5666 Vol. Issu 4: 3-4 BAYESAIN ESTIMATION OF SIZE BIASED CLASSICAL GAMMA DISTIBUTION J. A. sh A. Ahm a K. A. Mr 3 Dartmt o Statsts Uvrsty o Kashmr

More information

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

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

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

A Probabilistic Characterization of Simulation Model Uncertainties

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

More information

22 Nonparametric Methods.

22 Nonparametric Methods. 22 oparametrc Methods. I parametrc models oe assumes apror that the dstrbutos have a specfc form wth oe or more ukow parameters ad oe tres to fd the best or atleast reasoably effcet procedures that aswer

More information

Multiple Short Term Infusion Homework # 5 PHA 5127

Multiple Short Term Infusion Homework # 5 PHA 5127 Multipl Short rm Infusion Homwork # 5 PHA 527 A rug is aministr as a short trm infusion. h avrag pharmacokintic paramtrs for this rug ar: k 0.40 hr - V 28 L his rug follows a on-compartmnt boy mol. A 300

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

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

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

More information

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

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

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x)

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x) Objectve fucto f() : he optzato proble cossts of fg a vector of ecso varables belogg to the feasble set of solutos R such that It s eote as: Nolear optzato proble wthout costrats NPP: R f ( ) : R R f f

More information

3) Use the average steady-state equation to determine the dose. Note that only 100 mg tablets of aminophylline are available here.

3) Use the average steady-state equation to determine the dose. Note that only 100 mg tablets of aminophylline are available here. PHA 5127 Dsigning A Dosing Rgimn Answrs provi by Jry Stark Mr. JM is to b start on aminophyllin or th tratmnt o asthma. H is a non-smokr an wighs 60 kg. Dsign an oral osing rgimn or this patint such that

More information

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear

More information

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter

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

1985 AP Calculus BC: Section I

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

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

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

More information