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Scientific article
English

Saddlepoint Test in Measurement Error Models

Published inJournal of the American Statistical Association, vol. 106, no. 493, p. 147-156
Publication date2011
Abstract

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.

Keywords
  • Empirical distribution function
  • Kullback–Leibler divergence
  • Relative error
  • Semiparametric estimation
Citation (ISO format)
MA, Yanyuan, RONCHETTI, Elvezio. Saddlepoint Test in Measurement Error Models. In: Journal of the American Statistical Association, 2011, vol. 106, n° 493, p. 147–156. doi: 10.1198/jasa.2011.tm10031
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Article (Published version)
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ISSN of the journal0162-1459
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