You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
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
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
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