Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models

This paper is concerned with cross-validation (CV) criteria for choice of models, which can be regarded as approximately unbiased estimators for two types of risk functions. One is AIC type of risk or equivalently the expected Kullback-Leibler distance between the distributions of observations under...

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Bibliographic Details
Published in:Annals of the Institute of Statistical Mathematics 2003-09, Vol.55 (3), p.537-553
Main Authors: FUJIKOSHI, Yasunori, NOGUCHI, Takafumi, OHTAKI, Megu, YANAGIHARA, Hirokazu
Format: Article
Language:English
Subjects:
Asymptotic properties
Criteria
Exact sciences and technology
Inference from stochastic processes
time series analysis
Linear inference, regression
Mathematical models
Mathematics
Multivariate analysis
Nonparametric inference
Probability and statistics
Regression models
Sciences and techniques of general use
Statistics
Studies
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Summary:This paper is concerned with cross-validation (CV) criteria for choice of models, which can be regarded as approximately unbiased estimators for two types of risk functions. One is AIC type of risk or equivalently the expected Kullback-Leibler distance between the distributions of observations under a candidate model and the true model. The other is based on the expected mean squared error of prediction. In this paper we study asymptotic properties of CV criteria for selecting multivariate regression models and growth curve models under the assumption that a candidate model includes the true model. Based on the results, we propose their corrected versions which are more nearly unbiased for their risks. Through numerical experiments, some tendency of the CV criteria will be also pointed. [PUBLICATION ABSTRACT]
ISSN:0020-3157
1572-9052
DOI:10.1007/bf02517806