Saddlepoint Test in Measurement Error Models

• 成果类型: Article
• 署名作者: Ma, Yanyuan; Ronchetti, Elvezio
• 署名单位: Texas A&M University System; Texas A&M University College Station; University of Geneva; University of Geneva
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10031
• 发表日期: 2011
• 页码: 147-156
• 关键词: semiparametric estimators; approximations

摘要

We develop second-order hypothesis testing procedures in functional measurement error models for small or moderate sample sizes, where the classical first-order asymptotic analysis often fails to provide accurate results. In functional models no distributional assumptions are made on the unobservable covariates and this leads to semiparametric models. Our testing procedure is derived using saddlepoint techniques and is based on an empirical distribution estimation subject to the null hypothesis constraints, in combination with a set of estimating equations which avoid a distribution approximation. The validity of the method is proved in theorems for both simple and composite hypothesis tests, and is demonstrated through simulation and a farm size data analysis.