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On model selection consistency of M-estimators with geometrically decomposable penalties
model selection consistency M-estimators geometrically decomposable penalties
2013/6/14
Penalized M-estimators are used in many areas of science and engineering to fit models with some low-dimensional structure in high-dimensional settings. In many problems arising in bioinformatics, sig...
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
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...
Online Learning in a Contract Selection Problem
Online Learning Contract Selection Problem
2013/6/14
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitab...
Feature Selection Based on Term Frequency and T-Test for Text Categorization
feature selection term frequency t-test text classification
2013/6/14
Much work has been done on feature selection. Existing methods are based on document frequency, such as Chi-Square Statistic, Information Gain etc. However, these methods have two shortcomings: one is...
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Model Selection High-Dimensional Regression Generalized Irrepresentability Condition
2013/6/13
In the high-dimensional regression model a response variable is linearly related to $p$ covariates, but the sample size $n$ is smaller than $p$. We assume that only a small subset of covariates is `ac...
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...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
$l_{2,p}$ Matrix Norm and Its Application in Feature Selection
$l_{2,p}$ Matrix Norm Its Application Feature Selection
2013/5/2
Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ ...
Group-Sparse Model Selection: Hardness and Relaxations
Signal Approximation Structured Sparsity Interpretability Tractability Dynamic Programming Compressive Sensing
2013/5/2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these model...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Incorporating Employee Heterogeneity into Default Rules for Retirement Plan Selection
Incorporating Employee Heterogeneity Default Rules Retirement Plan Selection
2016/3/9
We study the effect of incorporating heterogeneity into default rules by examining the choice between retirement plans at a firm that transitioned from a defined benefit (DB) to a defined contribution...
Penalized Likelihood and Bayesian Function Selection in Regression Models
generalized additive model regularization smoothing spike and slab priors
2013/4/27
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...