A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models-Reference-Cited by-同舟云学术

A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

Published:2022-01-01 Issue:2 Volume:16 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Nguyen TrungTin¹,Nguyen Hien Duy²,Chamroukhi Faicel³,Forbes Florence⁴

Affiliation:

1. Normandie Univ, UNICAEN, CNRS, LMNO, 14000 Caen, France. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Inria Grenoble Rhone-Alpes, 655 av. de l’Europe, 38335 Montbonnot, France http://trung-tinnguyen.github.io/

2. Department of Mathematical and Physical Sciences, La Trobe University, Bundoora Melbourne 3066, Victoria Australia. School of Mathematics and Physics, University of Queensland, St. Lucia, Queensland, Australia https://hiendn.github.io/

3. Normandie Univ, UNICAEN, CNRS, LMNO, 14000 Caen, France. IRT SystemX, Palaiseau, France https://chamroukhi.com/

4. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Inria Grenoble Rhone-Alpes, 655 av. de l’Europe, 38335 Montbonnot, France http://mistis.inrialpes.fr/people/forbes/

Publisher

Institute of Mathematical Statistics

Subject

Statistics and Probability,Statistics, Probability and Uncertainty

Reference101 articles.

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3. LI, K.-C. (1991). Sliced inverse regression for dimension reduction. Journal of the American Statistical Association 86 316–327.

4. NGUYEN, X. (2013). Convergence of Latent Mixing Measures in Finite and Infinite Mixture Models. The Annals of Statistics 41 370–400.

5. AKAIKE, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 716–723.

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