Large sample convergence diagnostics for likelihood based inference: Logistic regression

Q Mathematics

Statistical Methodology Pub Date : 2016-12-01 DOI:10.1016/j.stamet.2016.08.001

Michael Brimacombe

引用次数: 3

Abstract

A general diagnostic approach to the evaluation of asymptotic approximation in likelihood based models is developed and applied to logistic regression. The expected asymptotic and observed log-likelihood functions are compared using a chi distribution in a directional Bayesian setting. This provides a general approach to assessing and visualizing non-convergence in higher dimensional models. Several well-known examples from the logistic regression literature are discussed.

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基于似然推理的大样本收敛诊断:逻辑回归

提出了一种基于似然模型的渐近逼近评估的一般诊断方法，并将其应用于逻辑回归。期望的渐近和观察到的对数似然函数在一个方向贝叶斯设置中使用chi分布进行比较。这为评估和可视化高维模型中的非收敛性提供了一种通用方法。讨论了逻辑回归文献中几个著名的例子。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Methodology STATISTICS & PROBABILITY-

CiteScore

0.59

自引率

0.00%

发文量

期刊介绍： Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.