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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...
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...
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...
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...
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...
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.
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...
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularizatio...
In linear regression problems with related predictors, it is desir-able to do variable selection and estimation by maintaining the hi-erarchical or structural relationships among predictors. In this p...
We propose MC+, a fast, continuous, nearly unbiased and accu- rate method of penalized variable selection in high-dimensional linear regression. The LASSO is fast and continuous, but biased. The bia...
Measurement error data or errors-in-variable data have been collected in many studies. Natural criterion functions are often unavailable for general functional measurement error models due to the la...
Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to co...
We revisit the adaptive Lasso in a high-dimensional linear model, and provide bounds for its prediction error and for its number of false positive selections. We compare the adaptive Lasso with an “...
Given n noisy samples with p dimensions, where n  p, we show that the multi-step thresholding procedure based on the Lasso – we call it the Thresholded Lasso, can accurately estimate a sparse vector ...
We consider the problem of estimating the number of components and the rel- evant variables in a mixture model for multilocus genotypic data. A new pe- nalized maximum likelihood criterion is propos...

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