Maximum likelihood estimation in logistic regression models with a diverging number of covariates

DOI10.1214/12-EJS731zbMath1295.62021MaRDI QIDQ1950882

Pang Du, Hua Liang

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1349355604

zbMATH Keywords

asymptotic normalitylogistic regressionhigh dimensionalinjective function

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Generalized linear models (logistic models) (62J12)

Related Items (8)

Frequentist model averaging for envelope models ⋮ Simplex-based Multinomial Logistic Regression with Diverging Numbers of Categories and Covariates ⋮ Asymptotic expansion of the posterior density in high dimensional generalized linear models ⋮ Corrigendum to: ``Maximum likelihood estimation in logistic regression models with a diverging number of covariates ⋮ Consistency of logistic classifier in abstract Hilbert spaces ⋮ The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square ⋮ Optimal model averaging for divergent-dimensional Poisson regressions ⋮ Asymptotic properties of GEE estimator for clustered ordinal data with high-dimensional covariates

Cites Work

• Unnamed Item
• Unnamed Item
• Sure independence screening in generalized linear models with NP-dimensionality
• Asymptotic normality and strong consistency of maximum quasi-likelihood estimates in generalized linear models
• Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models
• Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
• Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs
• High-dimensional generalized linear models and the lasso
• GEE analysis of clustered binary data with diverging number of covariates
• Best Subsets Logistic Regression
• Applying Generalized Linear Models
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems

This page was built for publication: Maximum likelihood estimation in logistic regression models with a diverging number of covariates