R. Meshkani
[ 1 ] - Plain Answers to Several Questions about Association/Independence Structure in Complete/Incomplete Contingency Tables
In this paper, we develop some results based on Relational model (Klimova, et al. 2012) which permits a decomposition of logarithm of expected cell frequencies under a log-linear type model. These results imply plain answers to several questions in the context of analyzing of contingency tables. Moreover, determination of design matrix and hypothesis-induced matrix of the model will be discusse...
[ 2 ] - Partial Association Components in Multi-way Contingency Tables and Their Statistiical Analysis
In analyses of contingency tables made up of categorical variables, the study of relationship between the variables is usually the major objective. So far, many association measures and association models have been used to measure the association structure present in the table. Although the association measures merely determine the degree of strength of association between the study varia...
[ 3 ] - Bayesian Estimation of the Multiple Change Points in Gamma Process Using X-bar chart
The process personnel always seek the opportunity to improve the processes. One of the essential steps for process improvement is to quickly recognize the starting time or the change point of a process disturbance. Different from the traditional normally distributed assumption for a process, this study considers a process which follows a gamma process. In addition, we consider the possibility o...
[ 4 ] - Empirical Bayes Estimation in Nonstationary Markov chains
Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical Bayes estimators for the transition probability matrix of a finite nonstationary Markov chain. The data are assumed to be of a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...
[ 5 ] - Regression Analysis under Inverse Gaussian Model: Repeated Observation Case
Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean. In this article, we propose a method for regression analysis under Inverse Gaussian model when th...
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