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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...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing Paid Search Advertising Partial Covariance
2016/1/20
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...
Tests for High Dimensional Regression Coeffcients with Factorial Designs
Factorial Design Gene-set test High dimensional regression
2016/1/19
We propose simultaneous tests for coefficients in high dimensional linear regression models with factorial designs. The proposed tests are designed for the “large p, small n” situations where the conv...
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...
Pivotal estimation in high-dimensional regression via linear programming
Pivotal estimation high-dimensional regression inear programming
2013/4/28
We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge o...
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....
Finite sample posterior concentration in high-dimensional regression
asymptotics Bayesian compressible prior high-dimensional posterior contraction regression shrinkage prior.
2012/9/19
We study the behavior of the posterior distribution in ultra high-dimensional Bayesian Gaussian linear regression models havingp佲n,withpthe number of predictors and nthe sample size. In particular, ou...
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
...