Practical Use of Higher Order Asymptotics for Multiparameter Exponential Families

D. Pierce, Dawn Peters
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引用次数: 152

Abstract

Recently developed asymptotics based on saddlepoint methods provide important practical methods for multiparameter exponential families, especially in generalized linear models. The aim here is to clarify and explore these. Attention is restricted to tests and confidence intervals regarding a single parametric function which can be represented as a natural parameter of a full rank exponential family. Excellent approximations to exact conditional inferences are often available, in terms of simple adjustments to the signed square root of the likelihood ratio statistic
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多参数指数族高阶渐近的实际应用
近年来基于鞍点方法的渐近研究为多参数指数族,特别是广义线性模型的渐近研究提供了重要的实用方法。本文的目的是澄清和探讨这些问题。注意仅限于检验和置信区间关于一个单参数函数,可以表示为一个全秩指数族的自然参数。通过对似然比统计量的带符号平方根的简单调整,通常可以获得精确条件推断的极好近似
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