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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Variable selection for generalized linear models with interval-censored failure time data
区间删失 失效时间数据 广义线性模型 变量选择
2023/5/9
Variable selection is often needed in many fields and has been discussed by many authors in various situations.
Regularization and variable selection via the elastic net
Grouping effect LARS algorithm Lasso Penalization p>n problem Variable selection
2015/8/21
We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar s...
SLOPE – Adaptive Variable Selection via Convex Optimization
Sparse regression variable selection false discovery rate lasso sorted `1 penalized estimation (SLOPE)
2015/6/17
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ +z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimat...
Bayesian variable selection for spatially dependent generalized linear models
generalized linear models variable selection Bayesian spatially
2012/11/22
Despite the abundance of methods for variable selection and accommodating spatial structure in regression models, there is little precedent for incorporating spatial dependence in covariate inclusion ...
Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
Bayes and empirical-Bayes multiplicity adjustment variable-selection problem
2010/11/15
This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens a...
Robust Bayesian variable selection with sub-harmonic priors
Bayes factor Bayesian variable selection fully Bayes method model selection consistency
2010/12/15
This paper studies Bayesian variable selection in linear models with spherically symmetric error distributions. We give a series of proper prior distributions which converge in a certain sense to an i...
A FAST PROCEDURE OF VARIABLE SELECTION IN LINEAR REGRESSION MODEL
Linear regression optimal subset a.s.l
2007/8/7
In many situations, we are interested in selection of important variables whichare adequate for prediction under a linear regression model. In this paper, a fast selection procedure is proposed and is...