COMPUTATIONAL ISSUES IN THE BAYESIAN-ANALYSIS OF CATEGORICAL-DATA - LOG-LINEAR AND GOODMAN RC MODEL

被引：0

作者：

EVANS, M ^{[1
]}

GILULA, Z ^{[1
]}

GUTTMAN, I ^{[1
]}

机构：

[1] HEBREW UNIV JERUSALEM, DEPT STAT, IL-91905 JERUSALEM, ISRAEL

来源：

STATISTICA SINICA | 1993年 / 3卷 / 02期

关键词：

ADAPTIVE IMPORTANCE SAMPLING; CATEGORICAL DATA; GOODMAN RC MODEL; LOGLINEAR MODELS; SINGULAR VALUE DECOMPOSITION;

D O I：

暂无

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：

The Baysian analysis of loglinear models requires the evaluation of high-dimensional integrals. Such an evaluation is frequently computationally prohibitive even with modern computers. We provide a parameterization of the loglinear model which renders these integrations amenable to the numerical methods of adaptive important sampling. This approach is applied in the analysis of two-way contingency tables using Goodman's RC model. We base the analysis on the full posterior distribution for the loglinear model and obtain the posterior distribution of a goodness-of-fit measure for Goodman's RC model.

引用

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页码：391 / 406

页数：16

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