搜索结果: 76-90 共查到“知识库 统计学 selection”相关记录133条 . 查询时间(0.183 秒)
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...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
feature selection optimistic bias naive Bayes models gene expression data
2009/9/22
For many classication and regression problems, a large number of
features are available for possible use this is typical of DNA microarray data
on gene expression, for example. Often, for computatio...
Discrete time portfolio selection with proportional transaction cost
PortfoIio selection Transaction costs Bellman equation
2009/9/22
In the paper discrete time portblio selection with
maximization of a discounted satisfaction functional is studied. In Section
2 the case without transaction costs is considered and explint
solutio...
Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
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...
LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances
Regression estimation statistical learning confidence regions shrinkage and thresholding methods LASSO
2009/9/16
We propose a general family of algorithms for regression estimation with quadratic loss, on the basis of geometrical considerations. These algorithms are able to select relevant functions into a large...
Adaptive estimation of linear functionals by model selection
Nonparametric regression white noise model adaptive estimation model selection pointwise adaptive estimation
2009/9/16
We propose an estimation procedure for linear functionals based on Gaussian model selection techniques. We show that the procedure is adaptive, and we give a non asymptotic oracle inequality for the r...
Estimation of Gaussian graphs by model selection
Gaussian graphical model Random matrices Model selection Penalized empirical risk
2009/9/16
We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from a $n$-sample of a Gaussian law $mathbb{P}_C$ in $mathbb{R}^p$ and f...
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 ...