Robust Deviance Information Criterion for Latent Variable Models
نویسندگان
چکیده
منابع مشابه
Robust Deviance Information Criterion for Latent Variable Models∗
It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular a...
متن کاملFast computation of the deviance information criterion for latent variable models
The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of the DIC for comparing latent variable models. In particular, the DIC calculated using the conditional likelihood (obtained by conditioning on the latent variables) is found to be inappropriate, whereas the DIC computed using the integrated likeli...
متن کاملDeviance Information Criterion for Comparing Stochastic Volatility Models
Bayesian methods have been ef cient in estimating parameters of stochastic volatility models for analyzing nancial time series. Recent advances made it possible to t stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components, and heavy-tailed distributions.However, a formal model comparison via Bayes factors remains dif cult. The main ob...
متن کاملA Robust and Diagnostic Information Criterion for Selecting Regression Models
We combine the selection of a statistical model with the robust parameter estimation and diagnostic properties of the Forward Search. As a result we obtain procedures that select the best model in the presence of outliers. We derive distributional properties of our method and illustrate it on data on ozone concentration. The effect of outliers on the choice of a model is revealed. Although our ...
متن کاملDeviance Information Criteria for Missing Data Models
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessment and model comparison is directly inspired by linear and generalised linear models, but it is open to different possible variations in the setting of missing data models, depending in particular on whether or not the missing variables are treated as parameters. In this paper, we reassess the c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2013
ISSN: 1556-5068
DOI: 10.2139/ssrn.2316341