Algorithms for approximate conditional inference

JOHN E. KOLASSA

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Algorithms for approximate conditional inference

机译：近似条件推断算法

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摘要

This paper presents a method for listing the sample space for a conditional distribution in a discrete generalized linear model. This tabulation is used in conjunction with saddlepoint methods to approximate the associated conditional probabilities. These probabilities are used to calculate conditional p-values.

机译：本文提出了一种方法，用于列出离散广义线性模型中条件分布的样本空间。该列表与鞍点方法结合使用，以近似关联的条件概率。这些概率用于计算条件p值。

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来源
《Statistics and computing》 |2003年第2期|p.121-126|共6页
作者
JOHN E. KOLASSA;
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作者单位

Department of Statistics, Rutgers University, Piscataway, NJ 08855, USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
exact conditional inference; saddlepoint approximations; canonical exponential family; linear programming; significance testing;

机译：精确的条件推断;加点逼近;规范的指数族;线性规划;显着性检验;

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Algorithms for approximate conditional inference

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