Sequential Variable Selection as Bayesian Pragmatism in Linear Factor Models
نویسندگان
چکیده
We examine a popular practitioner methodology used in the construction of linear factor models whereby particular factors are increased or decreased in relative importance within the model. This allows model builders to customise models and, as such, reflect those factors that the client and modeller may think important. We call this process Pragmatic Bayesianism (or prag-Bayes for short) and we provide analysis which shows when such a procedure is likely to be successful.
منابع مشابه
Bayesian Variable Selection via Particle Stochastic Search.
We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), whi...
متن کاملBayesian projection approaches to variable selection in generalized linear models
A Bayesian approach to variable selection which is based on the expected Kullback–Leibler divergence between the full model and its projection onto a submodel has recently been suggested in the literature. For generalized linear models an extension of this idea is proposed by considering projections onto subspaces defined via some form of L1 constraint on the parameter in the full model. This l...
متن کاملBayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملDECOUPLING SHRINKAGE AND SELECTION IN BAYESIAN LINEAR MODELS: A POSTERIOR SUMMARY PERSPECTIVE By P. Richard Hahn and Carlos M. Carvalho Booth School of Business and McCombs School of Business
LINEAR MODELS: A POSTERIOR SUMMARY PERSPECTIVE By P. Richard Hahn and Carlos M. Carvalho Booth School of Business and McCombs School of Business Selecting a subset of variables for linear models remains an active area of research. This paper reviews many of the recent contributions to the Bayesian model selection and shrinkage prior literature. A posterior variable selection summary is proposed...
متن کاملBayesian Model Selection: Some Thoughts on Future Directions
As advances in computational technology have made it possible to apply Bayesian methods to situations that are increasingly complex, using a wider variety of models that are increasingly more sophisticated, problems regarding model choice are now of central importance. As a result, the development of methods for model selection is now at the forefront of research in Bayesian statistics. This is...
متن کامل