Tuning Parameter Selection in Penalized Frailty Models

DOI10.1080/03610918.2014.968723zbMath1383.62174OpenAlexW2054169789MaRDI QIDQ2816680

Filia Vonta, Emmanouil Androulakis, Christos Koukouvinos

Publication date: 25 August 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://figshare.com/articles/dataset/Tuning_Parameter_Selection_in_Penalized_Frailty_Models/1305083

zbMATH Keywords

error estimationgeneralized cross validationclustered datavariable selectionpenalized likelihoodtuning parameterpenalized frailty model

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Censored data models (62N01)

Cites Work

• Asymptotic properties of criteria for selection of variables in multiple regression
• On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding
• One-step sparse estimates in nonconcave penalized likelihood models
• A posteriori error estimates for linear equations
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Variable selection for Cox's proportional hazards model and frailty model
• Robust inference for univariate proportional hazards frailty regression models
• Matrix Analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization Parameter Selections via Generalized Information Criterion
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• A study of Auchmuty's error estimate

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