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September, 1989 Bootstrapping the Maximum Likelihood Estimator in High-Dimensional Log-Linear Models
Wilhelm Sauermann
Ann. Statist. 17(3): 1198-1216 (September, 1989). DOI: 10.1214/aos/1176347264

Abstract

The notion of a bootstrap estimator of the distribution of the maximum likelihood estimator in log-linear models is defined for common sampling models. It is shown that the bootstrap estimator is consistent under assumptions which allow the dimension of the model to increase to infinity. Such an approach allows treatment of large, sparse contingency tables.

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Wilhelm Sauermann. "Bootstrapping the Maximum Likelihood Estimator in High-Dimensional Log-Linear Models." Ann. Statist. 17 (3) 1198 - 1216, September, 1989. https://doi.org/10.1214/aos/1176347264

Information

Published: September, 1989
First available in Project Euclid: 12 April 2007

zbMATH: 0683.62025
MathSciNet: MR1015146
Digital Object Identifier: 10.1214/aos/1176347264

Subjects:
Primary: 62G05
Secondary: 62H17

Rights: Copyright © 1989 Institute of Mathematical Statistics

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Vol.17 • No. 3 • September, 1989
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