research
∙
09/09/2023
Simultaneous Modeling of Disease Screening and Severity Prediction: A Multi-task and Sparse Regularization Approach
Disease prediction is one of the central problems in biostatistical rese...
share
research
∙
04/26/2023
Multi-Task Learning Regression via Convex Clustering
Multi-task learning (MTL) is a methodology that aims to improve the gene...
research
∙
04/15/2023
Multivariate regression modeling in integrative analysis via sparse regularization
The multivariate regression model basically offers the analysis of a sin...
research
∙
01/20/2022
Bayesian Fused Lasso Modeling via Horseshoe Prior
Bayesian fused lasso is one of the sparse Bayesian methods, which shrink...
research
∙
11/12/2021
Multi-task Learning for Compositional Data via Sparse Network Lasso
A network lasso enables us to construct a model for each sample, which i...
research
∙
10/18/2021
A Bayesian approach to multi-task learning with network lasso
Network lasso is a method for solving a multi-task learning problem thro...
research
∙
01/30/2021
Smoothly varying ridge regularization
A basis expansion with regularization methods is much appealing to the f...
research
∙
09/06/2020
Multilinear Common Component Analysis via Kronecker Product Representation
We consider the problem of extracting a common structure from multiple t...
research
∙
05/07/2020
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
In the variational relevance vector machine, the gamma distribution is r...
research
∙
03/30/2020
Variable fusion for Bayesian linear regression via spike-and-slab priors
In linear regression models, a fusion of the coefficients is used to ide...
research
∙
02/21/2020
Sparse principal component regression via singular value decomposition approach
Principal component regression (PCR) is a two-stage procedure: the first...
research
∙
11/20/2019
Bayesian sparse convex clustering via global-local shrinkage priors
Sparse convex clustering is to cluster observations and conduct variable...
research
∙
10/11/2019
Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization
We consider the problem of constructing a reduced-rank regression model ...
research
∙
09/28/2016
Sparse principal component regression for generalized linear models
Principal component regression (PCR) is a widely used two-stage procedur...
research
∙
02/16/2016
Bayesian generalized fused lasso modeling via NEG distribution
The fused lasso penalizes a loss function by the L_1 norm for both the r...
research
∙
02/26/2014
Sparse principal component regression with adaptive loading
Principal component regression (PCR) is a two-stage procedure that selec...
research
∙
03/20/2012
Selection of tuning parameters in bridge regression models via Bayesian information criterion
We consider the bridge linear regression modeling, which can produce a s...
research
∙
08/26/2011
Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions
This article addresses the problem of classification method based on bot...
research
∙
02/22/2011
