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.
Discrete Beta-Type Models
PUNZO, ANTONIO
2010-01-01
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.File in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
Punzo (2010) - Discrete Beta-Type Models.pdf
solo gestori archivio
Tipologia:
Versione Editoriale (PDF)
Licenza:
Non specificato
Dimensione
1.49 MB
Formato
Adobe PDF
|
1.49 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.