A Penalty Function Approach to Smoothing Large Sparse Contingency Tables
A Penalty Function Approach to Smoothing Large Sparse Contingency Tables
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 …