30 citations · 102 across the 11 of their papers we have counts for
11 papers
Mixed Models and Shrinkage Estimation for Balanced and Unbalanced Designs
Yihan Bao, James G. Booth
The known connection between shrinkage estimation, empirical Bayes, and mixed effects models is explored and applied to balanced and unbalanced designs in which the responses are c…
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…
Rank-based approach for estimating correlations in mixed ordinal data
Xiaoyun Quan, James G. Booth, Martin T. Wells
High-dimensional mixed data as a combination of both continuous and ordinal variables are widely seen in many research areas such as genomic studies and survey data analysis. Estim…
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…
Penalized versus constrained generalized eigenvalue problems
Irina Gaynanova, James Booth, Martin T. Wells
We investigate the difference between using an penalty versus an constraint in generalized eigenvalue problems, such as principal component analysis and discrimin…