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An approach to nonparametric smoothing techniques for regressions with discrete data

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This paper proposes nonparametric regression estimation techniques for small samples in situations where the dependent variable involves count data. Often the form of a kernel will not matter asymptotically. However, in small samples the kernel structure may play a more important role in approximating the small sample distribution especially for discrete random variables. In particular for count data we introduce a Poisson kernel regression estimator and a binomial kernel regression estimator. These new regression methods are applied to coal mine wildcat strike data. We use cross validation to evaluate out-of-sample performance.
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Document Type: Research Article

Affiliations: University of Notre Dame, Notre Dame, IN 46556, USA

Publication date: 2006-02-20

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