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Saddlepoint Test in Measurement Error Models
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DOI：10.1198/jasa.2011.tm10031.png)
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摘要
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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.
Keyword:
Empirical distribution function
Kullback-Leibler divergence
Relative error
Semiparametric estimation
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