A minimum variance kernel estimator and a discrete frequency polygon estimator for ordinal contingency tables
Document Type
Article
Publication Date
1-1-1996
Abstract
This paper introduces two estimators, a boundary corrected minimum variance kernel estimator based on a uniform kernel and a discrete frequency polygon estimator, for the cell probabilities of ordinal contingency tables. Simulation results show that the minimum variance boundary kernel estimator has a smaller average sum of squared error than the existing boundary kernel estimators. The discrete frequency polygon estimator is simple and easy to interpret, and it is competitive with the minimum variance boundary kernel estimator. It is proved that both estimators have an optimal rate of convergence in terms of mean sum of squared error. The estimators are also defined for high-dimensional tables. Copyright © 1996 by Marcel Dekker, Inc.
Publication Title
Communications in Statistics - Theory and Methods
Recommended Citation
Dong, J.,
&
Ye, Q.
(1996).
A minimum variance kernel estimator and a discrete frequency polygon estimator for ordinal contingency tables.
Communications in Statistics - Theory and Methods,
25(12), 3217-3245.
http://doi.org/10.1080/03610929608831894
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/9235