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Discrete Beta-Type Models

  • Antonio PunzoEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Beta Distribution Smoothing Parameter Kernel Estimator Beta Density Interpretable Parameterization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Economia e Metodi QuantitativiUniversità di CataniaCataniaItaly

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