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
error estimationgeneralized cross validationclustered datavariable selectionpenalized likelihoodtuning parameterpenalized frailty model
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
This page was built for publication: Tuning Parameter Selection in Penalized Frailty Models