1
1735-1294
Statistical Research and Training Center - Statistical Centre of Iran
251
General
Bayesian Test of Different Association Structures in Two-Way Contingency Tables
Saberi
Zahra
1
9
2017
14
1
1
18
06
04
2016
01
07
2017
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.
252
General
On the Efficiency of the Maximum Likelihood and Maximum Quasi-Likelihood Estimators in the Second Order Markov Chains
Mohammadpour
Mehrnaz
Nematollahi
Alireza
yaripour
Mohammadjavad
1
9
2017
14
1
19
30
08
08
2016
03
06
2017
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.
253
General
Reconstruction of Past Failure Times for Left Type-II Censored Data from Weibull Model
Asgharzadeh
Akbar
Mirzazadeh Ganji
Zahra
Valiollahi
Reza
Ahmadi
Jafar
1
9
2017
14
1
31
51
18
09
2016
07
06
2017
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.
254
General
Bayesian Analysis of Regression Models Using Instrumental Variables: A Case Study (Iranian Rural Households Income and Expenditure Data)
Akhgari
Omid
Golalizadeh
Mousa
1
9
2017
14
1
53
75
09
10
2016
03
05
2017
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.
255
General
An Efficient Method for Estimating Population Parameters Using Split Questionnaire Design
Kamgar
Saeideh
Navvabpour
Hamidreza
1
9
2017
14
1
77
99
16
10
2016
04
07
2017
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.
256
General
Bayesian Analysis of Augmented Mixed Beta Models with Skew-Normal Random Effects
Fallah Mohsenkhani
Zohreh
Mohammadzadeh
Mohsen
Baghfalaki
Taban
1
9
2017
14
1
101
118
06
11
2016
04
06
2017
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.