The problem of evaluating the goodness of the predictive distributions devel-oped by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the expected log-likelihood of the predictive distributions (Ando (2007a)), we develop the Bayesian predictive information crite-rion (BPIC). According to the numerical examples, we show that the posterior mean of the log-likelihood has a positive bias comparing with the posterior mean of the expected log-likelihood, and that the bias estimate of BPIC is close to the true bias. One of the advantages of BPIC is that we can optimize the size of Occam’s razor. Monte Carlo simulation results show that the proposed method performs well. Key words and phra...
Logistic regression is the standard method for assessing predictors of diseases. In logistic regress...
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging ...
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process i...
This paper investigates the performance of the predictive distributions of Bayesian models. To overc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
The standard practice of selecting a single model from some class of models and then making inferenc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
The standard methodology when building statistical models has been to use one of several algorithms ...
Many statistical scenarios initially involve several candidate models that describe the data-generat...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
This paper studies the asymptotic relationship between Bayesian model averaging and post-selection f...
Abstract: We consider nonparametric Bayesian estimation of a probabil-ity density p based on a rando...
Logistic regression is the standard method for assessing predictors of diseases. In logistic regress...
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging ...
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process i...
This paper investigates the performance of the predictive distributions of Bayesian models. To overc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
The standard practice of selecting a single model from some class of models and then making inferenc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
The standard methodology when building statistical models has been to use one of several algorithms ...
Many statistical scenarios initially involve several candidate models that describe the data-generat...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayes...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
This paper studies the asymptotic relationship between Bayesian model averaging and post-selection f...
Abstract: We consider nonparametric Bayesian estimation of a probabil-ity density p based on a rando...
Logistic regression is the standard method for assessing predictors of diseases. In logistic regress...
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging ...
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process i...