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A Penalty Function Approach to Smoothing Large Sparse Contingency Tables
Jeffrey S. Simonoff
The Annals of Statistics
Vol. 11, No. 1 (Mar., 1983), pp. 208-218
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2240474
Page Count: 11
You can always find the topics here!Topics: Estimators, Density estimation, Penalty function, Maximum likelihood estimation, Statism, Statistical estimation, Consistent estimators, Data smoothing, Estimators for the mean, Simulations
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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.
The Annals of Statistics © 1983 Institute of Mathematical Statistics