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March, 1983 A Penalty Function Approach to Smoothing Large Sparse Contingency Tables
Jeffrey S. Simonoff
Ann. Statist. 11(1): 208-218 (March, 1983). DOI: 10.1214/aos/1176346071

Abstract

Probabilities in a large sparse contingency table are estimated by maximizing the likelihood modified by a roughness penalty. It is shown that if certain smoothness criteria on the underlying probability vector are met, the estimator proposed is consistent in a one-dimensional table under a sparse asymptotic framework. Suggestions are made for techniques to apply the estimator in practice, and generalization to higher dimensional tables is considered.

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Jeffrey S. Simonoff. "A Penalty Function Approach to Smoothing Large Sparse Contingency Tables." Ann. Statist. 11 (1) 208 - 218, March, 1983. https://doi.org/10.1214/aos/1176346071

Information

Published: March, 1983
First available in Project Euclid: 12 April 2007

zbMATH: 0527.62043
MathSciNet: MR684878
Digital Object Identifier: 10.1214/aos/1176346071

Subjects:
Primary: 62G05
Secondary: 62E20

Keywords: Large sparse contingency tables , maximum penalized likelihood , smoothing of probability estimates , sparse asymptotics

Rights: Copyright © 1983 Institute of Mathematical Statistics

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Vol.11 • No. 1 • March, 1983
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