A link-free sparse group variable selection method for single-index model-Reference-Cited by-同舟云学术

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A link-free sparse group variable selection method for single-index model

Published:2016-11-14 Issue:13 Volume:44 Page:2388-2400
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zeng Bilin¹,Wen Xuerong Meggie²,Zhu Lixing³

Affiliation:

1. Department of Mathematics, California State University, Bakersfield, CA, USA

2. Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, USA

3. Department of Mathematics, Hong Kong Baptist University, Hong Kong, People's Republic of China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2016.1254731

Reference42 articles.

1. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

2. Shrinkage inverse regression estimation for model-free variable selection

3. Regression Graphics

5. Clustering cancer gene expression data: a comparative study

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1. Clustering of variables for enhanced interpretability of predictive models.;Informatica;2021-12-17

2. Bi-level feature selection in high dimensional AFT models with applications to a genomic study;Statistical Applications in Genetics and Molecular Biology;2019-09-17

3. Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size;Bioinformatics;2019-04-01

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