Pushpa Publishing House

Journal Menu

Content

Volume 69 (2025)

Volume 68 (2024)

Volume 67 (2023)

Volume 63 (2021)

Volume 62 (2021)

Volume 61 (2021)

Volume 60 (2020)

Volume 59 (2020)

Volume 58 (2020)

Volume 57 (2019)

Volume 56 (2019)

Volume 55 (2019)

Volume 54 (2018)

Volume 53 (2017)

Volume 52 (2016)

Volume 51 (2015)

Volume 50 (2015)

Volume 49 (2014)

Volume 48 (2014)

Volume 47 (2014)

Volume 46 (2014)

Volume 45 (2013)

Volume 44 (2013)

Volume 43 (2013)

Volume 42 (2013)

Volume 41 (2012)

Volume 40 (2012)

Volume 39 (2012)

Volume 38 (2012)

Volume 37 (2011)

Volume 36 (2011)

Volume 35 (2011)

Volume 34 (2011)

Volume 33 (2010)

Volume 32 (2010)

Volume 31 (2010)

Volume 30 (2010)

Volume 29 (2009)

Volume 28 (2009)

Volume 27 (2009)

Volume 26 (2008)

Volume 25 (2008)

Volume 24 (2008)

Volume 23 (2007)

Volume 22 (2007)

Volume 21 (2007)

Volume 20 (2006)

Volume 19 (2006)

Volume 19, Issue 2
Pg 141 - 292 (July 2006)

Volume 18 (2006)

Volume 17 (2005)

Volume 16 (2005)

Volume 15 (2005)

Volume 14 (2004)

Volume 13 (2004)

Volume 12 (2004)

Volume 11 (2003)

Volume 10 (2003)

Volume 9 (2003)

Volume 8 (2002)

Volume 7 (2002)

Volume 6 (2002)

Volume 5 (2001)

Volume 4 (2000)

Volume 3 (1999)

Volume 2 (1998)

Notice: All new articles and volumes will be published only on our new website — www.pphmjopenaccess.com

Far East Journal of Theoretical Statistics

Far East Journal of Theoretical Statistics
Volume 19, Issue 2, Pages 281 - 292 (July 2006)

A COMPARISON OF PARAMETER ESTIMATES IN STANDARD LOGISTIC REGRESSION USING WINBUGS MCMC AND MLE METHODS IN R FOR DIFFERENT SAMPLE SIZES
Masoud Karimlou (Iran), Gholamraza Jandaghi (Iran), Kazem Mohammad (Iran), Rory Wolfe (Australia) and Kamal Azam (Iran)

Abstract:
Estimation of parameters in statistical models is a serious concern among statisticians. Frequentists and Bayesians propose different approaches. In this paper we examine different approaches using MCMC and MLE methods to estimate parameters of standard logistic regression with two covariates to compare their efficiency for different sample sizes. We used WinBUGS MCMC and R MLE estimation methods.

The result showed that by increasing the sample size, the MCMC estimates get closer to MLE’s and the MCMC standard errors decrease as the sample size increases.

Keywords and phrases: logistic regression, MCMC, MLE, WinBUGS, R, sample size.

Number of Downloads: 314 | Number of Views: 731

Previous

P-ISSN: 0972-0863

E-ISSN: 3048-7358

Journal Stats

Publication count: 850

Citation count (Google Scholar): 0

h10-index (Google Scholar): 0

h-index (Google Scholar): 0

Downloads : 218462

Views: 853653

Downloads/publish articles: 257.01

Citations (Google Scholar)/publish articles: 0

This website is best viewed at 1024x768 or higher resolution with Microsoft Internet Explorer 6 or newer.