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- Discussion of “Sure Independence Screening for Ultra-High Dimensional . . .
Fan and Lv successfully tackled the extremely challenging case, where log (p) = O (nξ), ξ > 0 The proposed Sure Independence Screening (SIS) is a state of the art method for high dimensional variable screening: simple, powerful, and having optimal properties
- ON SURE SCREENING WITH MULTIPLE RESPONSES - Sinica
We systematically study variable screening methods for multi-response data First, we consider extensions of several popular screening methods to deal with multiple responses Each of these methods has its own clear drawbacks
- ON SURE SCREENING WITH MULTIPLE RESPONSES
We systematically study variable screening methods for multi-response data First, we consider extensions of several popular screening methods to deal with multiple responses Each of these methods has its own clear drawbacks
- A Generic Sure Independence Screening Procedure - PMC
Specifically, we consider six important aspects, including multivariate response, group predictor, survival response, collinear predicts, predictor interaction, nonlinear model, and robustness
- On Sure Screening with Multiple Responses - Sinica
We systematically study variable screening methods for multi-response data First, we consider extensions of several popular screening methods to deal with multiple responses Each of these methods has its own clear drawbacks
- Sure screening by ranking the canonical correlations | TEST - Springer
The sure independence screening procedure by ranking the marginal Pearson correlation is well documented in literatures and works satisfactorily in the ultra-high dimensional case
- Feature screening for multiple responses - ScienceDirect
To remove the constraints and develop a model-free screening procedure for multiple responses, we propose a novel correlation measure, termed multiple explained variability (MEV), and further introduce a MEV-based sure independence screening procedure, referred to as MEV-SIS
- Sure independence screening in the presence of missing data - Springer
In this paper we consider the case when observations of the predictors are missing at random We propose performing screening using the marginal linear correlation coefficient between each predictor and the response variable accounting for the missing data using maximum likelihood estimation
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