Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates

Size: px
Start display at page:

Download "Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates"

Transcription

1 Joural of Moder Appled Statstcal Methods Volume Issue Artcle Comparso of Parameters of Logormal Dstrbuto Based O the Classcal ad Posteror Estmates Raja Sulta Uversty of Kashmr, Sragar, Ida, hamzasulta8@yahoo.com S. P. Ahmad Uversty of Kashmr, Sragar, Ida, sprvz@yahoo.com Follow ths ad addtoal works at: Part of the Appled Statstcs Commos, Socal ad Behavoral Sceces Commos, ad the Statstcal Theory Commos Recommeded Ctato Sulta, Raja ad Ahmad, S. P. (03) "Comparso of Parameters of Logormal Dstrbuto Based O the Classcal ad Posteror Estmates," Joural of Moder Appled Statstcal Methods: Vol. : Iss., Artcle 8. DOI: 0.37/jmasm/ Avalable at: Ths Regular Artcle s brought to you for free ad ope access by the Ope Access Jourals at DgtalCommos@WayeState. It has bee accepted for cluso Joural of Moder Appled Statstcal Methods by a authorzed edtor of DgtalCommos@WayeState.

2 Joural of Moder Appled Statstcal Methods November 03, Vol., No., Copyrght 03 JMASM, Ic. ISSN Comparso of Parameters of Logormal Dstrbuto Based O the Classcal & Posteror Estmates Raja Sulta Uversty of Kashmr Sragar, Ida S. P. Ahmad Uversty of Kashmr Sragar, Ida Logormal dstrbuto s wdely used scetfc feld, such as agrcultural, etomologcal, bology etc. If a varable ca be thought as the multplcatve product of some postve depedet radom varables, the t could be modelled as logormal. I ths study, mamum lkelhood estmates ad posteror estmates of the parameters of logormal dstrbuto are obtaed ad usg these estmates we calculate the pot estmates of mea ad varace for makg comparsos. Keywords: Logormal dstrbuto, mamum lkelhood estmato, posteror estmates & R software Itroducto Atchso & Brow (957) have gve a very comprehesve treatmet of logormal dstrbuto. The logormal dstrbuto arses varous dfferet cotets such as physcs (dstrbuto of partcles due to pulversato); ecoomcs (come dstrbutos); bology (growth of orgasms), etc. Epste (947), Browlee (949), Delaporte (950), Moroey (95) descrbes varous applcatos of logormal dstrbuto to physcal ad dustral processes, tetle research ad qualty cotrol. I the cotet of lfe testg ad relablty problems, the logormal dstrbuto aswers a crtcsm sometmes rased agast the use of ormal dstrbuto (ragg from - to +) as a model for the falure tme dstrbuto whch must rage from 0 to. A radom varable X s sad to have a logormal dstrbuto f U log e X has ormal dstrbuto wth mea µ ad varace. Thus, the pdf of logormal dstrbuto s gve by Dr. Sulta s a research scholar the Departmet of Statstcs. Emal hm at: hamzasulta8@yahoo.com. Dr. Ahmad s a Assstat Professor of the Departmet of Statstcs. Emal hm at: sprvz@yahoo.com. 304

3 SULTAN & AHMAD f ( ) ep( (log ) ),, 0, 0 µ µ π < < > < () The lkelhood fucto of the radom sample (,, 3,..., ) T would be L( µ, ) ep( (log µ ) ) π () The mea ad varace of the logormal dstrbuto are gve by ad EX α ep µ + (3) V( X) ep µ + ep( ) (4) Mamum Lkelhood Estmators Mamum Lkelhood s a popular estmato techque for may dstrbutos because t pcks the values of the dstrbuto's parameters that make the data more lkely" tha ay other values of the parameters would make them. Ths s accomplshed by mamzg the lkelhood fucto of the parameters gve the data. Cosder the estmato of the parameters α ad. Let U log,,,,. The usg the fact that (U, U,..., U ) s a radom sample from Normal dstrbuto wth parameters (µ, ). The mle of µ ad frst are gve by ˆ µ U U (5) ad 305

4 COMPARISON OF PARAMETERS OF LOGNORMAL DISTRIBUTION ˆ (6) U U The mle of α ad are gve by ˆ ˆ α ˆ ep µ + (7) ad ˆ ep ˆ µ + ˆ ep ˆ (8) Posteror estmato of the parameter Aga, cosder the estmato of the parameters α ad. Frst obta the posteror estmates of µ ad ad the smultaeously the posteror estmates for α ad wll be obtaed. Laplace (774) foud that t worked eceptoally well to smply always choose the pror probablty dstrbuto for the parameter(s) of the model to be costat o the parameter space. The jot pror pdf for µ ad cosdered s P µ, (9) by Accordg to Bayes theorem, Jot posteror desty of µ ad Posteror desty pror desty* lkelhood (, ) P,. P, π µ µ µ s gve From equato () ad (9) the jot posteror desty of µ ad s gve by (, ) ep ( log ) π µ µ π 306

5 SULTAN & AHMAD log c π ( µ, ) ep ep µ ( ) (0) where ( log ) log ad c s a ormalzg costat. Ldley (96) eplaed f P ( θ ) be the pror ad P( θ ) posteror pdf P( θ ) s gve by P ( θ ) cp ( θ). P ( θ) be the lkelhood, the, where c s the ormalzg costat. The the value of c s obtaed by c P. P( ) θ θ dθ Therefore, c ca be obtaed by (, ) c π µ dµ d 0 log c d d o ep ep µ µ / Usg the trasformato t µ log 307

6 COMPARISON OF PARAMETERS OF LOGNORMAL DISTRIBUTION c ep π d / o ( ) c 3 Γ π 3 c ( ) 3 3 π 3 Γ () From the equato (0) 3 log π ( µ, ) ep ep µ 3 π 3 / Γ ( ) () Margal posteror destes of µ ad The margal desty of µ s obtaed by tegratg out from () ad s gve as ( ) (, ) π µ π µ d 0 308

7 SULTAN & AHMAD 309 / 0 log ep c d π µ µ + log c π µ µ Γ + log 3, B π µ µ + (3) The margal desty of s obtaed by tegratg the jot posteror desty of µ ad gve () over the rage of µ. It s gve as / log ep ep c d π µ µ / ep log ep c d π µ µ

8 COMPARISON OF PARAMETERS OF LOGNORMAL DISTRIBUTION ( ) π ep π c / ( ) ( ) π 3 ep 3 / 3 Γ ( ) (4) Posteror estmates of µ ad The margal desty of µ s gve (3) s a studet s t pdf. Thus the posteror estmates of µ s gve as ( ) * µ dµ µ E µ 3 B, log µ + log Usg the trasformato t µ 3 log * dt µ 3 3 B, t

9 SULTAN & AHMAD * µ log (5) whch s the posteror estmate for µ uder uform pror. Now the posteror estmate of ca be obtaed from equato (4) as 3 ep * d ( ) Γ ep 3 * d Γ ( ) 5 * (6) Thus, the posteror estmates of α ad are gve by * log * * α ep µ + ep + ( 5) (7) ad * * * * ep µ + ep 3

10 COMPARISON OF PARAMETERS OF LOGNORMAL DISTRIBUTION log ep + ep 5 5 * (8) Smulato study ad dscusso The estmates of the mea ad varace usg MLE ad Bayesa estmato was obtaed above. Net to obta s the umercal relatoshp of pot estmates usg true value of the parameters, MLE ad Bayesa estmato. I ths study, samples of 0, 0, 30, 40 ad 50 observatos were geerated from logormal pdf wth parameters µ ad. The smulatos were doe R Software. The mea ad varace were calculated to compare the methods of estmato. The results are preseted Table. I Table, whe pot estmates of logormal dstrbuto are compared usg true values of parameters wth MLE ad Bayesa estmato (by usg uform pror), the best estmator s the Mamum Lkelhood (MLE) because t has the mmum varace. Table. Pot estmates of logormal dstrbuto compared usg true values of parameters wth MLE ad Bayesa estmato True values MLE Posteror estmates Mea Varace Mea Varace Mea Varace ˆ ) ( α ) ( ) ( α ) ( ) ( ˆα ) ( * * Refereces Atchso, J., & Brow, J. A. C. (957). The logormal dstrbuto: Wth specal referece to ts uses ecoomcs. Cambrdge: Uversty Press. 3

11 SULTAN & AHMAD Browlee, K. A. (949). Idustral epermetato. d ed. Brookly: Chemcal Pub. Co. Delaporte, P. (950). Etude statstque sur les propretes des fotes. Revue De L'sttut Iteratoal De Statstque / Revew Of The Iteratoal Statstcal Isttute, (3/4), 6. do:0.307/40035 Epste. B. (947). The mathematcal descrpto of certa breakage mechasm leadg to the logarthmc-ormal dstrbuto. Joural of Frakl Isttute, 44, Laplace, P. S. (774). Mémore sur la probablté des causes par les évéemes. Académe royale des sceces (Frace), 6, Ldley, D. V. (96). Itroducto to probablty ad statstcs from a Bayesa vewpot: Part, Iferece, Aberystwyth: Uversty College of Wales. Moroey, M. J. (95). Facts from fgures. Baltmore, MD: Pegu Books. 33

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

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

More information

Bayesian Inferences for Two Parameter Weibull Distribution Kipkoech W. Cheruiyot 1, Abel Ouko 2, Emily Kirimi 3

Bayesian Inferences for Two Parameter Weibull Distribution Kipkoech W. Cheruiyot 1, Abel Ouko 2, Emily Kirimi 3 IOSR Joural of Mathematcs IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume, Issue Ver. II Ja - Feb. 05, PP 4- www.osrjourals.org Bayesa Ifereces for Two Parameter Webull Dstrbuto Kpkoech W. Cheruyot, Abel

More information

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION Iteratoal Joural of Mathematcs ad Statstcs Studes Vol.4, No.3, pp.5-39, Jue 06 Publshed by Europea Cetre for Research Trag ad Developmet UK (www.eajourals.org BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL

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

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

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved.

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved. VOL., NO., November 0 ISSN 5-77 ARPN Joural of Scece ad Techology 0-0. All rghts reserved. http://www.ejouralofscece.org Usg Square-Root Iverted Gamma Dstrbuto as Pror to Draw Iferece o the Raylegh Dstrbuto

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

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

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION

BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION Mathematcal ad Computatoal Applcatos, Vol. 7, No., pp. 29-38, 202 BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION Durdu Karasoy Departmet of Statstcs, Hacettepe Uversty, 06800 Beytepe, Akara,

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Some Statistical Inferences on the Records Weibull Distribution Using Shannon Entropy and Renyi Entropy

Some Statistical Inferences on the Records Weibull Distribution Using Shannon Entropy and Renyi Entropy OPEN ACCESS Coferece Proceedgs Paper Etropy www.scforum.et/coferece/ecea- Some Statstcal Ifereces o the Records Webull Dstrbuto Usg Shao Etropy ad Rey Etropy Gholamhosse Yar, Rezva Rezae * School of Mathematcs,

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Estimation of the Loss and Risk Functions of Parameter of Maxwell Distribution

Estimation of the Loss and Risk Functions of Parameter of Maxwell Distribution Scece Joural of Appled Mathematcs ad Statstcs 06; 4(4): 9- http://www.scecepublshggroup.com/j/sjams do: 0.648/j.sjams.060404. ISSN: 76-949 (Prt); ISSN: 76-95 (Ole) Estmato of the Loss ad Rsk Fuctos of

More information

Estimation and Testing in Type-II Generalized Half Logistic Distribution

Estimation and Testing in Type-II Generalized Half Logistic Distribution Joural of Moder Appled Statstcal Methods Volume 13 Issue 1 Artcle 17 5-1-014 Estmato ad Testg Type-II Geeralzed Half Logstc Dstrbuto R R. L. Katam Acharya Nagarjua Uversty, Ida, katam.rrl@gmal.com V Ramakrsha

More information

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad

More information

Module 7. Lecture 7: Statistical parameter estimation

Module 7. Lecture 7: Statistical parameter estimation Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER II STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for

More information

Confidence Intervals for Double Exponential Distribution: A Simulation Approach

Confidence Intervals for Double Exponential Distribution: A Simulation Approach World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Physcal ad Mathematcal Sceces Vol:6, No:, 0 Cofdece Itervals for Double Expoetal Dstrbuto: A Smulato Approach M. Alrasheed * Iteratoal Scece

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Study of Correlation using Bayes Approach under bivariate Distributions

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

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Bootstrap Method for Testing of Equality of Several Coefficients of Variation

Bootstrap Method for Testing of Equality of Several Coefficients of Variation Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee

More information

A new Family of Distributions Using the pdf of the. rth Order Statistic from Independent Non- Identically Distributed Random Variables

A new Family of Distributions Using the pdf of the. rth Order Statistic from Independent Non- Identically Distributed Random Variables Iteratoal Joural of Cotemporary Mathematcal Sceces Vol. 07 o. 8 9-05 HIKARI Ltd www.m-hkar.com https://do.org/0.988/jcms.07.799 A ew Famly of Dstrbutos Usg the pdf of the rth Order Statstc from Idepedet

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

A NEW GENERALIZATION OF ERLANG DISTRIBUTION WITH BAYES ESTIMATION

A NEW GENERALIZATION OF ERLANG DISTRIBUTION WITH BAYES ESTIMATION Iteratoal Joural of Iovatve Research ad Revew ISSN: 347 444 Ole Ole Iteratoal Joural valable at http://www.cbtech.org/jrr.htm 06 Vol. 4 prl-jue pp.4-9/bhat et al. Research rtcle NEW GENERLIZTION OF ERLNG

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

Minimax Estimation of the Parameter of the Burr Type Xii Distribution

Minimax Estimation of the Parameter of the Burr Type Xii Distribution Australa Joural of Basc ad Appled Sceces, 4(1): 6611-66, 1 ISSN 1991-8178 Mmax Estmato of the Parameter of the Burr Type X Dstrbuto Masoud Yarmohammad ad Hassa Pazra Departmet of Statstcs, Payame Noor

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

BAYESIAN ESTIMATION OF GUMBEL TYPE-II DISTRIBUTION

BAYESIAN ESTIMATION OF GUMBEL TYPE-II DISTRIBUTION Data Scece Joural, Volume, 0 August 03 BAYESIAN ESTIMATION OF GUMBEL TYPE-II DISTRIBUTION Kamra Abbas,*, Jayu Fu, Yca Tag School of Face ad Statstcs, East Cha Normal Uversty, Shagha 004, Cha Emal-addresses:*

More information

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for

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

MYUNG HWAN NA, MOON JU KIM, LIN MA

MYUNG HWAN NA, MOON JU KIM, LIN MA BAYESIAN APPROACH TO MEAN TIME BETWEEN FAILURE USING THE MODULATED POWER LAW PROCESS MYUNG HWAN NA, MOON JU KIM, LIN MA Abstract. The Reewal process ad the No-homogeeous Posso process (NHPP) process are

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Statistical modelling and latent variables (2)

Statistical modelling and latent variables (2) Statstcal modellg ad latet varables (2 Mxg latet varables ad parameters statstcal erece Trod Reta (Dvso o statstcs ad surace mathematcs, Departmet o Mathematcs, Uversty o Oslo State spaces We typcally

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Some Applications of the Resampling Methods in Computational Physics

Some Applications of the Resampling Methods in Computational Physics Iteratoal Joural of Mathematcs Treds ad Techoloy Volume 6 February 04 Some Applcatos of the Resampl Methods Computatoal Physcs Sotraq Marko #, Lorec Ekoom * # Physcs Departmet, Uversty of Korca, Albaa,

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

THE ROYAL STATISTICAL SOCIETY 2010 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 2 STATISTICAL INFERENCE

THE ROYAL STATISTICAL SOCIETY 2010 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 2 STATISTICAL INFERENCE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE STATISTICAL INFERENCE The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67. Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please

More information

Bias Correction in Estimation of the Population Correlation Coefficient

Bias Correction in Estimation of the Population Correlation Coefficient Kasetsart J. (Nat. Sc.) 47 : 453-459 (3) Bas Correcto Estmato of the opulato Correlato Coeffcet Juthaphor Ssomboothog ABSTRACT A estmator of the populato correlato coeffcet of two varables for a bvarate

More information

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty

More information

Goodness of Fit Test for The Skew-T Distribution

Goodness of Fit Test for The Skew-T Distribution Joural of mathematcs ad computer scece 4 (5) 74-83 Artcle hstory: Receved ecember 4 Accepted 6 Jauary 5 Avalable ole 7 Jauary 5 Goodess of Ft Test for The Skew-T strbuto M. Magham * M. Bahram + epartmet

More information

The Generalized Inverted Generalized Exponential Distribution with an Application to a Censored Data

The Generalized Inverted Generalized Exponential Distribution with an Application to a Censored Data J. Stat. Appl. Pro. 4, No. 2, 223-230 2015 223 Joural of Statstcs Applcatos & Probablty A Iteratoal Joural http://dx.do.org/10.12785/jsap/040204 The Geeralzed Iverted Geeralzed Expoetal Dstrbuto wth a

More information

STATISTICAL INFERENCE

STATISTICAL INFERENCE (STATISTICS) STATISTICAL INFERENCE COMPLEMENTARY COURSE B.Sc. MATHEMATICS III SEMESTER ( Admsso) UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION CALICUT UNIVERSITY P.O., MALAPPURAM, KERALA, INDIA -

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Interval Estimation of a P(X 1 < X 2 ) Model for Variables having General Inverse Exponential Form Distributions with Unknown Parameters

Interval Estimation of a P(X 1 < X 2 ) Model for Variables having General Inverse Exponential Form Distributions with Unknown Parameters Amerca Joural of Theoretcal ad Appled Statstcs 08; 7(4): 3-38 http://www.scecepublshggroup.com/j/ajtas do: 0.648/j.ajtas.080704. ISSN: 36-8999 (Prt); ISSN: 36-9006 (Ole) Iterval Estmato of a P(X < X )

More information

9.1 Introduction to the probit and logit models

9.1 Introduction to the probit and logit models EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

3. Basic Concepts: Consequences and Properties

3. Basic Concepts: Consequences and Properties : 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Reliability Based Design Optimization with Correlated Input Variables

Reliability Based Design Optimization with Correlated Input Variables 7--55 Relablty Based Desg Optmzato wth Correlated Iput Varables Copyrght 7 SAE Iteratoal Kyug K. Cho, Yoojeog Noh, ad Lu Du Departmet of Mechacal & Idustral Egeerg & Ceter for Computer Aded Desg, College

More information

arxiv: v1 [math.st] 24 Oct 2016

arxiv: v1 [math.st] 24 Oct 2016 arxv:60.07554v [math.st] 24 Oct 206 Some Relatoshps ad Propertes of the Hypergeometrc Dstrbuto Peter H. Pesku, Departmet of Mathematcs ad Statstcs York Uversty, Toroto, Otaro M3J P3, Caada E-mal: pesku@pascal.math.yorku.ca

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Chapter 8: Statistical Analysis of Simulated Data

Chapter 8: Statistical Analysis of Simulated Data Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample

More information

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,

More information

A NEW LOG-NORMAL DISTRIBUTION

A NEW LOG-NORMAL DISTRIBUTION Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Regresso What s a Model? 1. Ofte Descrbe Relatoshp betwee Varables 2. Types - Determstc Models (o radomess) - Probablstc Models (wth radomess) EPI 809/Sprg 2008 9 Determstc Models 1. Hypothesze

More information

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 59, 2947-2951 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/ma.2013.310259 O the Iterval Zoro Symmetrc Sgle Step Procedure IZSS1-5D for the Smultaeous

More information

Generating Multivariate Nonnormal Distribution Random Numbers Based on Copula Function

Generating Multivariate Nonnormal Distribution Random Numbers Based on Copula Function 7659, Eglad, UK Joural of Iformato ad Computg Scece Vol. 2, No. 3, 2007, pp. 9-96 Geeratg Multvarate Noormal Dstrbuto Radom Numbers Based o Copula Fucto Xaopg Hu +, Jam He ad Hogsheg Ly School of Ecoomcs

More information

Confidence Interval Estimations of the Parameter for One Parameter Exponential Distribution

Confidence Interval Estimations of the Parameter for One Parameter Exponential Distribution IAENG Iteratoal Joural of Appled Mathematcs, 45:4, IJAM_45_4_3 Cofdece Iterval Estmatos of the Parameter for Oe Parameter Epoetal Dstrbuto Juthaphor Ssomboothog Abstract The objectve of ths paper was to

More information

ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES

ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES Mahmoud Rad Mahmoud Isttute of Statstcs, Caro Uversty Suza Mahmoud Mohammed Faculty

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Multiple Linear Regression Analysis

Multiple Linear Regression Analysis LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple

More information

IFYMB002 Mathematics Business Appendix C Formula Booklet

IFYMB002 Mathematics Business Appendix C Formula Booklet Iteratoal Foudato Year (IFY IFYMB00 Mathematcs Busess Apped C Formula Booklet Related Documet: IFY Mathematcs Busess Syllabus 07/8 IFYMB00 Maths Busess Apped C Formula Booklet Cotets lease ote that the

More information

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing Iteratoal Joural of Computer Applcatos (0975 8887) (Mote Carlo) Resamplg Techque Valdty Testg ad Relablty Testg Ad Setawa Departmet of Mathematcs, Faculty of Scece ad Mathematcs, Satya Wacaa Chrsta Uversty

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Line Fitting and Regression

Line Fitting and Regression Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots

More information

Bayesian Inference for Logit-Model using Informative and Non-informative Priors

Bayesian Inference for Logit-Model using Informative and Non-informative Priors ISSN 684-843 Joural of Statstcs Volume, 4. pp. 6-74 Abstract Bayesa Iferece for Logt-Model usg Iformatve ad No-formatve Prors Tahr Abbas Malk ad Muhammad Aslam I the feld of ecoometrcs aalyss of bary data

More information

A Note on Ratio Estimators in two Stage Sampling

A Note on Ratio Estimators in two Stage Sampling Iteratoal Joural of Scetfc ad Research Publcatos, Volume, Issue, December 0 ISS 0- A ote o Rato Estmators two Stage Samplg Stashu Shekhar Mshra Lecturer Statstcs, Trdet Academy of Creatve Techology (TACT),

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Extreme Value Theory: An Introduction

Extreme Value Theory: An Introduction (correcto d Extreme Value Theory: A Itroducto by Laures de Haa ad Aa Ferrera Wth ths webpage the authors ted to form the readers of errors or mstakes foud the book after publcato. We also gve extesos for

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

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.?

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.? Ch 4. Statstcs 4.1 Quattatve aalyss requres : soud kowledge of chemstry : possblty of terfereces WHY do we eed to use STATISTICS Aal. Chem.? ucertaty ests. wll we accept ucertaty always? f ot, from how

More information

ECON 5360 Class Notes GMM

ECON 5360 Class Notes GMM ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

A NEW MODIFIED GENERALIZED ODD LOG-LOGISTIC DISTRIBUTION WITH THREE PARAMETERS

A NEW MODIFIED GENERALIZED ODD LOG-LOGISTIC DISTRIBUTION WITH THREE PARAMETERS A NEW MODIFIED GENERALIZED ODD LOG-LOGISTIC DISTRIBUTION WITH THREE PARAMETERS Arbër Qoshja 1 & Markela Muça 1. Departmet of Appled Mathematcs, Faculty of Natural Scece, Traa, Albaa. Departmet of Appled

More information

Order statistics from non-identical Standard type II Generalized logistic variables and applications at moments

Order statistics from non-identical Standard type II Generalized logistic variables and applications at moments Amerca Joural of Theoretcal ad Appled Statstcs 05; 4(: -5 Pulshed ole Jauar 3, 05 (http://www.scecepulshggroup.com//atas do: 0.648/.atas.05040. ISSN: 36-8999 (Prt; ISSN: 36-9006 (Ole Order statstcs from

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

GENERALIZED METHOD OF MOMENTS CHARACTERISTICS AND ITS APPLICATION ON PANELDATA

GENERALIZED METHOD OF MOMENTS CHARACTERISTICS AND ITS APPLICATION ON PANELDATA Sc.It.(Lahore),26(3),985-990,2014 ISSN 1013-5316; CODEN: SINTE 8 GENERALIZED METHOD OF MOMENTS CHARACTERISTICS AND ITS APPLICATION ON PANELDATA Beradhta H. S. Utam 1, Warsoo 1, Da Kurasar 1, Mustofa Usma

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 2

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 2 THE ROYAL STATISTICAL SOCIETY 06 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE The Socety s provdg these solutos to assst caddates preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

A Bivariate Distribution with Conditional Gamma and its Multivariate Form

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

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information