TY - JOUR
T1 - Saddlepoint test in measurement error models
AU - Ma, Yanyuan
AU - Ronchetti, Elvezio
N1 - Funding Information:
Yanyuan Ma is Associate Professor, Department of Statistics, Texas A&M University, College Station, TX 77843-3143 (E-mail: [email protected]). Elvezio Ronchetti is Professor, Research Center for Statistics and Department of Econometrics, University of Geneva, 1211 Geneva, Switzerland (E-mail: [email protected]). The authors would like to thank the editor, the associate editor, two referees, and N. Wang for very constructive and helpful comments. The research of the first author was partially supported by a U.S. NSF grant DMS-0906341. The second author thanks the Department of Statistics, Texas A&M University for the hospitality during part of this research and the Swiss National Science Foundation Pro*Doc Program 114533 for partial financial support.
PY - 2011/3
Y1 - 2011/3
N2 - 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.
AB - 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.
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U2 - 10.1198/jasa.2011.tm10031
DO - 10.1198/jasa.2011.tm10031
M3 - Article
AN - SCOPUS:79954561686
SN - 0162-1459
VL - 106
SP - 147
EP - 156
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 493
ER -