Mathematics High dimensional variable selection and covariance matrix estimation PRINCETON UNIVERSITY LvJinchiThis thesis presents new results on two important statistical problems: high dimensional variable selection and high dimensional covariance matrix estimation. Challenges of these problems lie in the ...
High Dimensional Variable Selection 来自 Semantic Scholar 喜欢 0 阅读量: 13 作者: D Guetta 摘要: 'Model selection' is a statistical learning problem in which we use a set of input vectors X and a set of matching output numbers Y to 'learn' something about the relationship between the input...
We approached the problem from a high dimensional variable selection perspective. In particular, we can select variants with varying, constant and zero coefficients, which correspond to cases of G×E interaction, no G×E interaction and no genetic effect, respectively. The procedure was implemented ...
The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems...
高维线性模型的变量选择Variable selection of high dimensional linear model.pdf,: : 2007 : : : : ()(sparsity) Fan Lv(J.Fan,J.Lv. Sure independence screening for ultra-high dimensional feature space[J]. Journal of the Royal Statistical Society Series B.2008
Variable selection is a difficult problem that is particularly challenging in the analysis of high-dimensional genomic data. Here, we introduce the CAR score, a novel and highly effective criterion for variable ranking in linear regression based on Mahalanobis-decorrelation of the explanatory variables....
Inference for feature selection using the Lasso with high-dimensional data Penalized regression models such as the Lasso have proved useful for variableselection in many fields - especially for situations with high-dimensional dat... K Brink-Jensen,CT Ekstrøm - 《Eprint Arxiv》 被引量: 1发表...
III. [STATS] Variable Selection, Confounder Correction, and other problems in High Dimensional Data Keywords: statistics, variable selection, lasso, confounder correction, linear mixed model, high d…
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency We consider the problem of high-dimensional variable selection: given noisy observations of a -sparse vector , estimate the subset of non-zero entries of ... D Omidiran,MJ Wainwright -...
We propose a new two-stage "screen and clean" procedure for assessing the uncertainties pertaining to the selection of relevant variables in high-dimensional regression problems. In this two-stage method, screening consists in selecting a subset of candidate variables by a sparsity-inducing penalized...