Donald R. Williams | Searcharxiv

arXiv2019

BFpack: Flexible Bayes Factor Testing of Scientific Theories in R

There has been a tremendous methodological development of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development is due to the flexibility of the Bayes factor for testing multiple hypotheses simultaneously, the ability to test complex hypotheses involving equality as well as order constraints on the parameters of interest, and the interpretability of the outcome as the weight of evidence provided by the data in support of competing scientific theories. The available software tools for Bayesian hypothesis testing are still limited however. In this paper we present a new R-package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools (i) for Bayesian exploratory testing (null vs positive vs negative effects), (ii) for Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) for common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random.

stat.CO↗

arXiv2018

Bayesian Estimation of Gaussian Graphical Models with Predictive Covariance Selection

Gaussian graphical models are used for determining conditional relationships between variables. This is accomplished by identifying off-diagonal elements in the inverse-covariance matrix that are non-zero. When the ratio of variables (p) to observations (n) approaches one, the maximum likelihood estimator of the covariance matrix becomes unstable and requires shrinkage estimation. Whereas several classical (frequentist) methods have been introduced to address this issue, fully Bayesian methods remain relatively uncommon in practice and methodological literatures. Here we introduce a Bayesian method for estimating sparse matrices, in which conditional relationships are determined with projection predictive selection. With this method, that uses Kullback-Leibler divergence and cross-validation for neighborhood selection, we reconstruct the inverse-covariance matrix in both low and high-dimensional settings. Through simulation and applied examples, we characterized performance compared to several Bayesian methods and the graphical lasso, in addition to TIGER that similarly estimates the inverse-covariance matrix with regression. Our results demonstrate that projection predictive selection not only has superior performance compared to selecting the most probable model and Bayesian model averaging, particularly for high-dimensional data, but also compared to the the Bayesian and classical glasso methods. Further, we show that estimating the inverse-covariance matrix with multiple regression is often more accurate, with respect to various loss functions, and efficient than direct estimation. In low-dimensional settings, we demonstrate that projection predictive selection also provides competitive performance. We have implemented the projection predictive method for covariance selection in the R package GGMprojpred

stat.AP↗