The author extends to the Bayesian nonparametric context the multinomial goodness-of fit tests due to Cressie & Read (1984). Her approach is suitable when the model of interest is a discrete distribution. She provides an explicit form for the tests, which are based on power-divergence measures between a prior Dirichlet process which is highly concentrated aruond the model of interest and the corresponding posterior Dirichlet process. In addition to providing interesting special cases and and useful approximations, she discusses calibration and choice of test through examples

A family of power divergence diagnostics for goodness of fit

CAROTA, Cinzia
2007

Abstract

The author extends to the Bayesian nonparametric context the multinomial goodness-of fit tests due to Cressie & Read (1984). Her approach is suitable when the model of interest is a discrete distribution. She provides an explicit form for the tests, which are based on power-divergence measures between a prior Dirichlet process which is highly concentrated aruond the model of interest and the corresponding posterior Dirichlet process. In addition to providing interesting special cases and and useful approximations, she discusses calibration and choice of test through examples
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http://archimede.mat.ulaval.ca/cjs
Bayesian inference; Dirichlet process mixtures; Goodness-of-fit; Nonparametric alternatives; Power-divergence measures.
C. Carota
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/27458
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