A Comparative Study of Kernel-Based Density Estimates for Categorical Data
作者:
D.M. Titterington,
期刊:
Technometrics
(Taylor Available online 1980)
卷期:
Volume 22,
issue 2
页码: 259-268
ISSN:0040-1706
年代: 1980
DOI:10.1080/00401706.1980.10486142
出版商: Taylor & Francis Group
关键词: Density estimation;Kernel functions;Multivariate categorical data;Incomplete data;Cross-validation;Smoothing;Discrimination
数据来源: Taylor
摘要:
Kernel estimates of discrete probabilities are considered, with emphasis on computation of the smoothing parameters. Different approaches based on minimum mean squared error, cross-validation and pseudo-Bayesian techniques are compared, particularly from the points of view of reliability and ease of computation. The advantages of a fractional allocation procedure and of computing the bandwidths marginally for each variable are pointed out. Multicategory variables and incomplete data can be coped with. The relationship between the kernel method and other smoothing techniques for categorical data is discussed.
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