搜索结果: 16-30 共查到“统计学 variable selection”相关记录38条 . 查询时间(0.147 秒)
Tight conditions for consistent variable selection in high dimensional nonparametric regression
Tight conditions for consistent variable selection high dimensional nonparametric regression
2011/3/23
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the num...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
variable selection high dimensional nonparametric regression
2011/3/22
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the nu...
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...
A Review of Bayesian Variable Selection Methods: What, How and Which
Variable Selection MCMC BUGS
2009/9/24
The selection of variables in regression problems has occupied the
minds of many statisticians.Several Bayesian variable selection methods have been
developed,and we concentrate on the following met...
Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors
Bayes factor Conditional Predictive Ordinate Conjugate prior Poisson regression Logistic regression
2009/9/22
In this paper, we consider theoretical and computational connections
between six popular methods for variable subset selection in generalized linear
models (GLMs) Under the conjugate priors develope...
Honest variable selection in linear and logistic regression models via $ell_1$ and $ell_1 + ell_2$ penalization
penalty sparse consistent variable selection regression generalized linear models logistic regression
2009/9/16
This paper investigates correct variable selection in finite samples via $ell_1$ and $ell_1 + ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows fro...
Variable selection for multicategory SVM via adaptive sup-norm regularization
Classification L1-norm penalty multicategory sup-norm SVM
2009/9/16
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...
Structured variable selection in support vector machines
Classification Heredity Nonparametric estimation Support vector machine Variable selection
2009/9/16
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The ...