搜索结果: 1-15 共查到“理论统计学 variable selection”相关记录22条 . 查询时间(0.408 秒)
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...
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...
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 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...
The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Model selection lasso loss rank principle shrinkage parameter variable se-lection
2010/11/9
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...
Nearly unbiased variable selection under minimax concave penalty
Variable selection model selection penalized estimation leastsquares correct selection minimax unbiasedness mean squared error
2010/3/10
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...
Variable selection in measurement error models
errors in variables estimating equations measurement error models non-concavepenalty function SCAD semi-parametric methods
2010/3/10
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...
Ultrahigh dimensional variable selection for Cox's proportional hazards model
Ultrahigh dimensional variable selection Cox's proportional hazards model
2010/3/10
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...
Prediction and variable selection with the adaptive Lasso
adaptive Lasso prediction restricted eigenvalue thresholding variable selection
2010/3/9
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 “...
Thresholded Lasso for high dimensional variable selection and statistical estimation
Linear regression Lasso Gauss-Dantzig Selector 1 regularization 0 penalty multiple-stepprocedure ideal model selection
2010/3/10
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 ...
A new penalized criterion for variable selection and clustering using genotypic data
Variables selection Penalized Likelihood Slope heuristics Mixture multinomial models Population genetics
2010/3/10
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...