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Factor profiling for ultra high dimensional variable selection
Bayesian Information Criterion Factor Profiling Forward Re- gression Maximum Eigenvalue Ratio Criterion Profiled Independent Screening
2016/1/19
We propose here a novel method of factor profiling (FP) for ultra high dimen-sional variable selection. The new method assumes that the correlation structure of the high dimensional data can be well r...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Sharp Variable Selection of a Sparse Submatrix in a High-Dimensional Noisy Matrix
estimation minimax testing random matrices selection of sparse signal sharp selection bounds variable selection
2013/4/28
We observe a $N\times M$ matrix of independent, identically distributed Gaussian random variables which are centered except for elements of some submatrix of size $n\times m$ where the mean is larger ...
Variable Selection for Clustering and Classification
Classication Cluster analysis High-dimensional data Mixture models Model-based clus-tering Variable selection
2013/4/28
As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that a...
Criteria for Bayesian model choice with application to variable selection
Model selection variable selection objective Bayes.
2012/11/23
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propos...
Criteria for Bayesian model choice with application to variable selection
Model selection variable selection objective Bayes.
2012/11/23
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propos...
Variable Selection with Exponential Weights and $l_0$-Penalization
Variable selection model selection sparse linear model xponential weights Gibbs sampler identifiability condition.
2012/9/17
In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed ...
Consistent selection of tuning parameters via variable selection stability
kappa coefficient penalized regression selection consistency stability tuning
2012/9/17
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
Grouped Variable Selection via Nested Spike and Slab Priors
Log-sum approximation Majorization-minimization algorithms
2011/7/6
In this paper we study grouped variable selection problems by proposing a specified prior, called the nested spike and slab prior, to model collective behavior of regression coefficients.
Tight conditions for consistency of variable selection in the context of high dimensionality
variable selection nonparametric regression set estimation sparsity pattern
2011/7/6
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of variables is very large. The main focus is on the situation where the numb...
Multiple Hypotheses Testing For Variable Selection
model selection FDR Lasso Bolasso multiple hypotheses testing
2011/7/6
Many methods have been developed to estimate the set of relevant variables in a sparse linear model Y= XB+e where the dimension p of B can be much higher than the length n of Y.
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
MCMC P-splines spike-and-slab prior normal-inverse-gamma
2011/6/20
The R package spikeSlabGAM implements Bayesian variable selection, model choice,
and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and
Poisson responses. Its purpose ...
Variable selection with error control: Another look at Stability Selection
Complementary Pairs Stability Selection r-concavity subagging subsampling variable selection
2011/6/20
Stability Selection was recently introduced by Meinshausen and B¨uhlmann (2010) as
a very general technique designed to improve the performance of a variable selection
algorithm. It is based on aggr...
