Abstract
A more interpretable parameterization of a beta density is the starting point to propose an analogous discrete beta (d. b. ) distribution assuming values on a finite set. Thus a smooth estimator using d. b. kernels is considered. By construction, it is both well-defined and free of boundary bias. Taking advantage of the discrete nature of the data, a technique of smoothing parameter selection is also proposed in moderate-to-large samples. Finally, a real data set is analyzed in order to appreciate the advantages of this nonparametric proposal.
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Punzo, A. (2010). Discrete Beta-Type Models. In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_27
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DOI: https://doi.org/10.1007/978-3-642-10745-0_27
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