Add to ORKGABSTRACT. A new method is proposed for variable screening, variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The method involves minimizing a penalized Euclidean distance, where the penalty is the geometric mean of the `1 and `2 norms of the regression coefficients. This particular formulation exhibits a grouping effect, which is useful for screening out predictors in higher or ultra-high dimensional problems. Also, an important result is a signal recovery theorem, which does not require an estimate of the noise standard deviation. Practical performances of variable selection and prediction are evaluated through simulation studies and the analys...
Penalized likelihood approaches are widely used for high-dimensional regression. Although many metho...
The paper considers a practically important generalization of the theory of regression. A linear fun...
Linear regression models are commonly used statistical models for predicting a response from a set o...
A method is introduced for variable selection and prediction in linear regression problems where the...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
With advanced capability in data collection, applications of linear regression analysis now often in...
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual ...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
In high dimensional regression problems penalization techniques are a useful tool for estimation and...
Summary. Variable selection can be challenging, particularly in situations with a large number of pr...
We propose penalized empirical likelihood for parameter estimation and variable selection for proble...
Recently, Hwang et al. (2009) proposed a variable selection method for high dimensional linear regre...
<p>We propose a penalized likelihood method to fit the linear discriminant analysis model when the p...
This article is concerned with screening features in ultrahigh-dimensional data analysis, which has ...
Penalized likelihood approaches are widely used for high-dimensional regression. Although many metho...
The paper considers a practically important generalization of the theory of regression. A linear fun...
Linear regression models are commonly used statistical models for predicting a response from a set o...
A method is introduced for variable selection and prediction in linear regression problems where the...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
With advanced capability in data collection, applications of linear regression analysis now often in...
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual ...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
In high dimensional regression problems penalization techniques are a useful tool for estimation and...
Summary. Variable selection can be challenging, particularly in situations with a large number of pr...
We propose penalized empirical likelihood for parameter estimation and variable selection for proble...
Recently, Hwang et al. (2009) proposed a variable selection method for high dimensional linear regre...
<p>We propose a penalized likelihood method to fit the linear discriminant analysis model when the p...
This article is concerned with screening features in ultrahigh-dimensional data analysis, which has ...
Penalized likelihood approaches are widely used for high-dimensional regression. Although many metho...
The paper considers a practically important generalization of the theory of regression. A linear fun...
Linear regression models are commonly used statistical models for predicting a response from a set o...