Asymptotics of AIC, BIC and Cp model selection rules in high-dimensional regression-Reference-Cited by-同舟云学术

Asymptotics of AIC, BIC and Cp model selection rules in high-dimensional regression

Published:2022-11-01 Issue:4 Volume:28 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Bai Zhidong¹,Choi Kwok Pui²,Fujikoshi Yasunori³,Hu Jiang¹

Affiliation:

1. KLASMOE and School of Mathematics & Statistics, Northeast Normal University, China

2. Department of Statistics and Data Science, National University of Singapore, Singapore

3. Department of Mathematics, Graduate School of Science, Hiroshima University, Japan

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference35 articles.

1. Anzanello, M.J. and Fogliatto, F.S. (2014). A review of recent variable selection methods in industrial and chemometrics applications. European J. of Industrial Engineering 8 619.

2. Bai, Z.D., Choi, K.P. and Fujikoshi, Y. (2018). Consistency of AIC and BIC in estimating the number of significant components in high-dimensional principal component analysis. Ann. Statist. 46 1050–1076. 10.1214/17-AOS1577

3. Bai, Z.D., Miao, B. and Pan, G. (2007). On asymptotics of eigenvectors of large sample covariance matrix. Ann. Probab. 35 1532–1572. 10.1214/009117906000001079

4. Bai, Z.D. and Silverstein, J.W. (2010). Spectral Analysis of Large Dimensional Random Matrices, 2nd ed. Springer Series in Statistics. New York: Springer. 10.1007/978-1-4419-0661-8

5. Bai, Z.D., Choi, K.P., Fujikoshi, Y. and Hu, J. (2022). Supplement to “Asymptotics of AIC, BIC and ${C_{p}}$ model selection rules in high-dimensional regression.” 10.3150/21-BEJ1422SUPP

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