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An approach to nonparametric smoothing techniques for regressions with discrete data

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Abstract:

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.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/00036840500368581

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

Publication date: February 20, 2006

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routledg/raef/2006/00000038/00000003/art00007
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