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Abstract Details

Activity Number: 124
Type: Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #305185
Title: Nonparametric Tests for Distribution Specification in Multiparameter Local Likelihood Models
Author(s): Kee-Hoon Kang*+ and Ming-Yen Cheng and Liang Peng
Companies: Hankuk University of Foreign Studies and National Taiwan University and Georgia Institute of Technology
Address: Department of Statistics, Yongin 449-791, , South Korea
Keywords: Gaussian process ; goodness of fit ; local modeling
Abstract:

Multiparameter local likelihood models have been accepted as a flexible tool for modeling the relationship between responses and covariates, and the corresponding methodology has been used to analyze data arising from climatology, environmetrics, finance, medicine, and so on. Although both point and interval estimation for the unknown parameter functions in the model have been investigated in the literature, how to formally test goodness-of-fit of the specified form of the conditional density function remains an unsolved problem. In this talk, we address this specification test problem. Our tests are developed using ideas of probability integral transformation and the well-known Kolmogorov-Smirnov and Cramer-von Mises test statistics. The asymptotic null distributions of the proposed test statistics depend on the unknown parameter functions, so bootstrap tests are suggested. We conduct a simulation study to assess finite sample properties of the proposed test and apply it to validate the generalized extreme value local likelihood model for an environmental data set.


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