Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
Bayesian Test of Different Association Structures in Two-Way Contingency Tables
1
18
FA
Zahra
Saberi
z-saberi@cc.iut.ac.ir
10.18869/acadpub.jsri.14.1.1
Bayesian methods for exact small-sample analysis with categorical data in $Itimes J$ contingency tables are considered. Different structures of association are defined and tested concerning log odds ratios in these tables with fixed row margins. The conditional distribution of sufficient statistics for interesting parameters conditional on the sufficient statistics of other nuisance parameters in the model is obtained and used to eliminate the effect of nuisance parameters. The resulting distribution for the table is Fisherchr('39')s multivariate noncentral hypergeometric distribution. For Bayesian approach, although computation under this distribution is complicated, a common Bayesian model is considered. Bayes factor is used as a measure of evidence for Bayesian testing of different association structures. The performance of our testing Bayesian approach is compared with that of the classical corrected likelihood ratio test by some simulation studies. Also the Bayesian test of “homogenous association” is applied on a real data set.
Bayes factor, homogenous association, small-sample
http://jsri.srtc.ac.ir/article-1-251-en.html
http://jsri.srtc.ac.ir/article-1-251-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
On the Efficiency of the Maximum Likelihood and Maximum Quasi-Likelihood Estimators in the Second Order Markov Chains
19
30
FA
Mehrnaz
Mohammadpour
m.mohammadpour@umz.ac.ir
Alireza
Nematollahi
nematollahi@susc.ac.ir
Mohammadjavad
yaripour
jyaripour@yahoo.com
10.18869/acadpub.jsri.14.1.19
The present work focuses on the second order Markov chain model which arises in a variety of settings and is well-suited to be modeled in many applications. The efficiency of the maximum quasi-likelihood estimators with the full maximum likelihood estimators for second order Markov chain models are given, besides the limiting normality results on the asymptotic properties of the associated estimates. Some efficiency calculations are also given to discuss the feasibility and computational complexity of the QL approach relative to the full likelihood approach.
Maximum likelihood, quasi-likelihood, second order Markov chain, efficiency
http://jsri.srtc.ac.ir/article-1-252-en.html
http://jsri.srtc.ac.ir/article-1-252-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
Reconstruction of Past Failure Times for Left Type-II Censored Data from Weibull Model
31
51
FA
Akbar
Asgharzadeh
a.asgharzadeh@umz.ac.ir
Zahra
Mirzazadeh Ganji
zmganji@gmail.com
Reza
Valiollahi
valiollahi.reza@gmail.com
Jafar
Ahmadi
ahmadi-j@um.ac.ir
10.18869/acadpub.jsri.14.1.31
This paper deals with the problem of reconstructing missing data in a left type-II censoring scheme, where the underlying distribution is the Weibull distribution. Frequentist and Bayesian approaches are adopted in order to provide some point reconstructors for the past failure times. The problem of determining reconstruction intervals for the past failure times is also considered. The investigation includes an example of application to real data and various comparisons based on Monte Carlo simulations.
Left censoring, maximum likelihood reconstructor, best unbiased and conditional median reconstructors, reconstruction interval
http://jsri.srtc.ac.ir/article-1-253-en.html
http://jsri.srtc.ac.ir/article-1-253-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
Bayesian Analysis of Regression Models Using Instrumental Variables: A Case Study (Iranian Rural Households Income and Expenditure Data)
53
75
FA
Omid
Akhgari
o.akhgari@modares.ac.ir
Mousa
Golalizadeh
golalizadeh@modares.ac.ir
10.18869/acadpub.jsri.14.1.53
The instrumental variable (IV) regression is a common model in econometrics and other applied disciplines. This model is one of the proper candidate in dealing with endogeneity phenomenon which occurs in analyzing the multivariate regression when the errors are correlated with some covariates. One can consider IV regression as a special case of simultaneous equation models (SEM). There are some cases in which the normality assumption might not hold for the error term in these models and so the skew-normal distribution might be a suitable candidate. The present paper tackle the Bayesian inference based on Markov Chain Monte Carlo (MCMC) using this density for the error term while some instrumental variables are considered in the corresponding regression model. The proposed model is utilized to analysis the Iranian rural households income and expenditure collected in 2009.
Instrumental variable, endogenous (exogenous) variable, bayesian inference, skew-normal distribution, Markov chain Monte Carlo logarithm
http://jsri.srtc.ac.ir/article-1-254-en.html
http://jsri.srtc.ac.ir/article-1-254-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
An Efficient Method for Estimating Population Parameters Using Split Questionnaire Design
77
99
FA
Saeideh
Kamgar
saeideh.kamgar@gmail.com
Hamidreza
Navvabpour
h.navvabpour@srtc.ac.ir J. Statist.
10.18869/acadpub.jsri.14.1.77
The effect of survey questionnaire length on precision of survey statistics has been discussed in several studies. It is generally concluded that the lengthy questionnaire leads to increase non-sampling errors, especially nonresponse rate. Split questionnaire method has been introduced as a solution to decrease the response burden and nonresponse rate, involves splitting the questionnaire into subquestionnaires and then administering these subquestionnaires to different subsets of the original sample. In this paper, we suggest a method for splitting long questionnaire and analyzing resulting data, using small area estimation. The general idea behind this approach is to construct some socio-demographic or geographic small areas to apply small area estimation to improve the efficiency of survey statistics.
Our new approach is supported by a simulation study based on a real dataset of the 2011 Iran Income and Expenditure survey, in which we show our method provides more reliable statistics than existing methods.
Empirical best linear unbiased prediction, matrix sampling, multiple imputation, response burden, small area estimation.
http://jsri.srtc.ac.ir/article-1-255-en.html
http://jsri.srtc.ac.ir/article-1-255-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
14
1
2017
9
1
Bayesian Analysis of Augmented Mixed Beta Models with Skew-Normal Random Effects
101
118
FA
Zohreh
Fallah Mohsenkhani
zohrehf@yahoo.com
Mohsen
Mohammadzadeh
mohsen_m@modares.ac.ir
Taban
Baghfalaki
t.baghfalaki@modares.ac.ir
10.18869/acadpub.jsri.14.1.101
Many studies in different areas include data in the form of rates or proportions that should be analyzed. The data may also accept values zero and one. Augmented beta regression models are an appropriate choice for continuous response variables in the closed unit interval [0,1]. The data in this model are based on a combination of three distributions, degenerate distribution at 0 and 1, and a beta density in (0,1). The random effects are usually added to the model for accommodating the data structures as well as correlation impacts. In most of these models, the random effects are generally assumed to be normally distributed, while this assumption is frequently violated in applied studies. In this paper, the augmented mixed beta regression model with skew-normal distributed random effects is presented. A Bayesian approach is adopted for parameter estimation using Markov Chain Monte Carlo method. The proposed model is applied to analyze a real data set from Labor Force Survey.
Augmented beta regression, beta distribution, mixed models, Bayesian approach, skew-normal distribution.
http://jsri.srtc.ac.ir/article-1-256-en.html
http://jsri.srtc.ac.ir/article-1-256-en.pdf