Dynamic association modeling in 2 x 2 contingency tables

被引：5

作者：

Ghoreishi, S. K. ^{[2
]}

Alijani, M. ^{[1
]}

机构：

[1] Cooperat Dev Bank, Dept Stat, Mazandaran, Iran

[2] Qom Univ, Dept Stat, Fac Sci, Qom, Iran

来源：

STATISTICAL METHODOLOGY | 2011年 / 8卷 / 02期

关键词：

Association models; Contingency tables; Markovian processes; MCMC method; Genetic algorithm; BAYESIAN-ESTIMATION; INFERENCE; SELECTION;

D O I：

10.1016/j.stamet.2010.10.002

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Association and dynamic association analyses in contingency tables, that take time into account, are of great interest in many disciplines. In this work, we define three dynamic association models: polynomial trend, Markovian, and sin-cos dependence models. We then discuss at length the classical and Bayesian analysis of these models. These results are then utilized to analyze postiche data, wherein we carry out the estimation of model parameters by using a genetic algorithm. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：242 / 255

页数：14

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