A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE

Qin Wang; Xiangrong Yin

doi:10.1016/j.csda.2008.03.003

ScienceGate Book Chapters

JOURNAL ARTICLE

A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE

Qin Wang Xiangrong Yin

Year: 2008 Journal: Computational Statistics & Data Analysis Vol: 52 (9)Pages: 4512-4520 Publisher: Elsevier BV

DOI: 10.1016/j.csda.2008.03.003

Get Full-Text PDF Get Analytical Report

Keywords:

Sufficient dimension reduction Mathematics Feature selection Dimension (graph theory) Covariate Dimensionality reduction Parametric statistics Model selection Variable (mathematics) Data set Algorithm Function (biology) Mathematical optimization Statistics Computer science Regression Artificial intelligence

Metrics

Cited By

2.16

FWCI (Field Weighted Citation Impact)

Refs

0.89

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE

Metrics

Citation History

Topics

Related Documents

Sparse Bayesian variable selection for classifying high-dimensional data

Model Identification and Variable Selection for High-Dimensional Sparse Data

Model Identification and Variable Selection for High-Dimensional Sparse Data

Sparse covariance thresholding for high-dimensional variable selection

A Variable Selection Method for High-Dimensional Survival Data