Variable selection of generalized regression models based on maximum rank correlation

DOI10.1007/S10255-014-0424-6zbMath1305.62277OpenAlexW2033085620MaRDI QIDQ477519

Peng-Jie Dai, Qing-Zhao Zhang, Zhi-Hua Sun

Publication date: 9 December 2014

Published in: Acta Mathematicae Applicatae Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10255-014-0424-6

zbMATH Keywords

adaptive LASSOoracle propertiesgeneralized regression modelsmaximum rank correlation estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10) Linear inference, regression (62J99)

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Sure joint feature screening in nonparametric transformation model for right censored data

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation
• Non-parametric analysis of a generalized regression model. The maximum rank correlation estimator
• Smoothed rank correlation of the linear transformation regression model
• Empirical Likelihood Confidence Regions in a Partially Linear Single-Index Model
• A semiparametric approach for the nonparametric transformation survival model with multiple covariates
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Penalized Spline Estimation for Partially Linear Single-Index Models
• Regularization Parameter Selections via Generalized Information Criterion
• The Limiting Distribution of the Maximum Rank Correlation Estimator
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• A Simplex Method for Function Minimization

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