Consistent Group Identification and Variable Selection in Regression With Correlated Predictors
نویسندگان
چکیده
منابع مشابه
Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.
Statistical procedures for variable selection have become integral elements in any analysis. Successful procedures are characterized by high predictive accuracy, yielding interpretable models while retaining computational efficiency. Penalized methods that perform coefficient shrinkage have been shown to be successful in many cases. Models with correlated predictors are particularly challenging...
متن کاملBayesian Variable Selection in Regression with Networked Predictors
We consider Bayesian variable selection in linear regression when the relationships among a possibly large number of predictors are described by a network given a priori. A class of motivating examples is to predict some clinical outcomes with high-dimensional gene expression profiles and a gene network, for which it is assumed that the genes neighboring to each other in the network are more li...
متن کاملConsistent group selection in high-dimensional linear regression.
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive method for variable selection since it respects the grouping structure in the data. We study the selection and estimation properties of the group Lasso in high-dimensional settings when the number of groups exceeds the sample size. We provide sufficient conditions under which the group Lasso selec...
متن کاملthe past hospitalization and its association with suicide attempts and ideation in patients with mdd and comparison with bmd (depressed type) group
چکیده ندارد.
Sparse regression with highly correlated predictors
We consider a linear regression y = Xβ + u where X ∈ Rn×p, p n, and β is s−sparse. Motivated by examples in financial and economic data, we consider the situation where X has highly correlated and clustered columns. To perform sparse recovery in this setting, we introduce the clustering removal algorithm (CRA), that seeks to decrease the correlation in X by removing the cluster structure withou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2013
ISSN: 1061-8600,1537-2715
DOI: 10.1080/15533174.2012.707849