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Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing
2016/1/26
In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically,we employ the partial covariances be...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing
2016/1/25
In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically,we employ the partial covariances be...
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Model Selection High-Dimensional Regression Generalized Irrepresentability Condition
2013/6/13
In the high-dimensional regression model a response variable is linearly related to $p$ covariates, but the sample size $n$ is smaller than $p$. We assume that only a small subset of covariates is `ac...
The Lasso for High-Dimensional Regression with a Possible Change-Point
Lasso oracleine qualities sample splitting sparsity threshold models
2012/11/23
We consider a high-dimensional regression model with a possible change-point due to a covariate threshold and develop the Lasso estimator of regression coefficients as well as the threshold parameter....
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
Adaptive Lasso High Dimensional Regression Gaussian Graphical Modeling
2010/3/18
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional
model selection in linear and Gaussian graphical models. Our conditions for consistency cover more
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