Cross-validation and the estimation of probability distributions with categorical data Academic Article

abstract

• In this paper, we consider the problem of estimating a joint distribution that is defined over a set of discrete variables. We use a smoothing kernel estimator to estimate the joint distribution. We allow for the case in which some of the discrete variables are uniformly distributed, and explicitly address the vector-valued smoothing parameter case due to its practical relevance. We show that the cross-validated smoothing parameters differ in their asymptotic behavior depending on whether a variable is uniformly distributed or not. We also discuss the mixed discrete and continuous variable case. Simulations show that the proposed estimator performs much better than the commonly used frequency estimator.

authors

• Li, Qi

published proceedings

• JOURNAL OF NONPARAMETRIC STATISTICS

author list (cited authors)

• Ouyang, D., Li, Q., & Racine, J.

citation count

• 36

complete list of authors

• Ouyang, D||Li, Q||Racine, J

publication date

• January 2006

publisher

• Taylor & Francis  Publisher

published in

• Journal of Nonparametric Statistics  Journal

keywords

• Cross-validation
• Discrete Variable
• Non-parametric Smoothing
• Probability Estimation

Digital Object Identifier (DOI)

• 10.1080/10485250600569002

start page

• 69

end page

• 100

volume

• 18

issue

• 1

URL

• http://dx.doi.org/10.1080/10485250600569002