18 citations · 44 across the 14 of their papers we have counts for
6 papers · 1 filter
On Graphical Models and Convex Geometry
Haim Bar, Martin T. Wells
We introduce a mixture-model of beta distributions to identify significant correlations among predictors when is large. The method relies on theorems in convex geometry, wh…
Mixed Effect Modeling and Variable Selection for Quantile Regression
Haim Bar, James Booth, Martin T. Wells
It is known that the estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights…
Quantile Regression Modelling via Location and Scale Mixtures of Normal Distributions
Haim Y. Bar, James G. Booth, Martin T. Wells
We show that the estimating equations for quantile regression can be solved using a simple EM algorithm in which the M-step is computed via weighted least squares, with weights com…
A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models
Haim Bar, James Booth, Martin T. Wells
A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of pu…
A Scalable Empirical Bayes Approach to Variable Selection
Haim Y. Bar, James G. Booth, Martin T. Wells
We develop a model-based empirical Bayes approach to variable selection problems in which the number of predictors is very large, possibly much larger than the number of responses…
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments
Haim Bar, James Booth, Elizabeth Schifano +1
A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in…