This paper is concerned with screening features in ultrahigh dimensional data anal-ysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the sure independence screening procedure based on the Pearson correlation (SIS, for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008
In a regression setting we propose algorithms that reduce the dimensionality of the fea-tures while ...
Understanding and developing a correlation measure that can detect general dependencies is not only ...
The importance of feature selection for statistical and machine learning models derives from their e...
This article is concerned with screening features in ultrahigh-dimensional data analysis, which has ...
High-dimensional data are commonly seen in modern statistical applications, variable selection metho...
<p>Kendall’s <i>τ</i> (right) correlation coefficients for different dimensionality reduction method...
<p>Pearson’s <i>r</i> correlation coefficients for different dimensionality reduction methods are sh...
Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensiona...
High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide ra...
DNA microarray datasets are characterized by a large number of features with very few samples, which...
This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual reco...
In this article, we propose a new metric, the so-called martingale difference correlation, to measur...
High Dimension Low Sample Size statistical analysis is becoming in-creasingly important in a wide ra...
A variable screening procedure via correlation learning was proposed by Fan and Lv (2008) to reduce ...
In this research, we investigate the sequential lasso method for feature selection in sparse high di...
In a regression setting we propose algorithms that reduce the dimensionality of the fea-tures while ...
Understanding and developing a correlation measure that can detect general dependencies is not only ...
The importance of feature selection for statistical and machine learning models derives from their e...
This article is concerned with screening features in ultrahigh-dimensional data analysis, which has ...
High-dimensional data are commonly seen in modern statistical applications, variable selection metho...
<p>Kendall’s <i>τ</i> (right) correlation coefficients for different dimensionality reduction method...
<p>Pearson’s <i>r</i> correlation coefficients for different dimensionality reduction methods are sh...
Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensiona...
High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide ra...
DNA microarray datasets are characterized by a large number of features with very few samples, which...
This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual reco...
In this article, we propose a new metric, the so-called martingale difference correlation, to measur...
High Dimension Low Sample Size statistical analysis is becoming in-creasingly important in a wide ra...
A variable screening procedure via correlation learning was proposed by Fan and Lv (2008) to reduce ...
In this research, we investigate the sequential lasso method for feature selection in sparse high di...
In a regression setting we propose algorithms that reduce the dimensionality of the fea-tures while ...
Understanding and developing a correlation measure that can detect general dependencies is not only ...
The importance of feature selection for statistical and machine learning models derives from their e...