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2003•SSRN Electronic JournalOpen access

A Comparison Among Bayes Factors for Separated Models: Some Simulation Results

Maria Ivanilde Araújo, Basílio de Bragança Pereira

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Abstract

Some alternative Bayes Factors: Intrinsic, Posterior and Fractional have been proposed to overcome the difficulties presented when prior information is weak and improper prior are used. Additional difficulties also appear when the models are separated or nonnested. This paper presents some simulation results comparing these Bayes Factors to discriminate among the Lognormal, Weibull, Gamma and exponential distributions.

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What this paper is about

Some alternative Bayes Factors: Intrinsic, Posterior and Fractional have been proposed to overcome the difficulties presented when prior information is weak and improper prior are used. Additional difficulties also appear when the models are separated or nonnested. This paper presents some simulation results comparing these Bayes Factors to discriminate among the Lognormal, Weibull, Gamma and exponential distributions.

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Available abstract

Some alternative Bayes Factors: Intrinsic, Posterior and Fractional have been proposed to overcome the difficulties presented when prior information is weak and improper prior are used. Additional difficulties also appear when the models are separated or nonnested. This paper presents some simulation results comparing these Bayes Factors to discriminate among the Lognormal, Weibull, Gamma and exponential distributions.

Key concepts: Weibull distribution, Bayes' theorem, Bayes factor, Statistics, Log-normal distribution, Econometrics, Mathematics, Exponential function

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