16 citations · 49 across the 14 of their papers we have counts for
14 papers
Supervised Dictionary Learning with Auxiliary Covariates
Joowon Lee, Hanbaek Lyu, Weixin Yao
Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a prior…
Method of Moments Confidence Intervals for a Semi-Supervised Two-Component Mixture Model
Bradley Lubich, Daniel Jeske, Weixin Yao
A mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture…
Pursuing Sources of Heterogeneity in Modeling Clustered Population
Yan Li, Chun Yu, Yize Zhao +3
Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are man…
Robust Model Selection for Finite Mixture of Regression Models Through Trimming
Sijia Xiang, Weixin Yao
In this article, we introduce a new variable selection technique through trimming for finite mixture of regression models. Compared to the traditional variable selection techniques…
Maximum Likelihood Estimation of a Semiparametric Two-component Mixture Model using Log-concave Approximation
Yangmei Zhou, Weixin Yao
Motivated by studies in biological sciences to detect differentially expressed genes, a semiparametric two-component mixture model with one known component is being studied in this…
An Overview of Semiparametric Extensions of Finite Mixture Models
Sijia Xiang, Weixin Yao, Guangren Yang
Finite mixture models have been a very important tool for exploring complex data structures in many scientific areas, for example, economics, epidemiology, finance. In the past dec…
Semiparametric Mixture Regression with Unspecified Error Distributions
Yanyuan Ma, Shaoli Wang, Lin Xu +1
In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood…
Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering
Sijia Xiang, Weixin Yao
In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture r…
Fully Bayesian Classification with Heavy-tailed Priors for Selection in High-dimensional Features with Grouping Structure
Lai Jiang, Longhai Li, Weixin Yao
Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to find the most useful genes that are related to a c…
Mixture of Regression Models with Single-Index
Sijia Xiang, Weixin Yao
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regress…
Nonparametric and Varying Coefficient Modal Regression
Weixin Yao, Sijia Xiang
In this article, we propose a new nonparametric data analysis tool, which we call nonparametric modal regression, to investigate the relationship among interested variables based o…
Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection
Longhai Li, Weixin Yao
High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of differ…
Robust Linear Regression: A Review and Comparison
Chun Yu, Weixin Yao, Xue Bai
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may h…
High-dimensional Feature Selection Using Hierarchical Bayesian Logistic Regression with Heavy-tailed Priors
Longhai Li, Weixin Yao
The problem of selecting the most useful features from a great many (eg, thousands) of candidates arises in many areas of modern sciences. An interesting problem from genomic resea…