搜索结果: 1-6 共查到“统计学 1-penalization”相关记录6条 . 查询时间(0.058 秒)
Variable Selection with Exponential Weights and $l_0$-Penalization
Variable selection model selection sparse linear model xponential weights Gibbs sampler identifiability condition.
2012/9/17
In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed ...
Censored quantile regression processes under dependence and penalization
quantile regression Bahadur representation variable selection weak convergence censored data dependent data
2012/9/18
We consider quantile regression processes from censored data under dependent data structures and derive a uniform Bahadur representation for those processes. We also consider cases where the dimension...
Estimation for High-Dimensional Linear Mixed-Effects Models Using l1-Penalization
Estimation High-Dimensional Linear Mixed-Effects Models l1-Penalization
2010/3/10
We propose an `1-penalized estimation procedure for high-dimensional lin-
ear mixed-effects models. The models are useful whenever there is a grouping
structure among high-dimensional observations, ...
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...
Model selection by resampling penalization
Non-parametric statistics resampling exchangeable weighted bootstrap model selection penalization non-parametric regression
2009/9/16
In this paper, a new family of resampling-based penalization procedures for model selection is defined in a general framework. It generalizes several methods, including Efron's bootstrap penalization ...
Model selection by resampling penalization。