Bootstrap Choice of Estimators in Parametric and Semiparametric Families: An Extension of EIC

DOI10.1111/1541-0420.00020zbMath1210.62033OpenAlexW2117863645WikidataQ52016356 ScholiaQ52016356MaRDI QIDQ3079095

C. Sakarovitch, Daniel Commenges, Bernoît Liquet

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00020

zbMATH Keywords

bootstrapsmoothingKullback-Leibler informationregressionsemiparametric

Mathematics Subject Classification ID

Related Items (6)

Forecasting ARMA models: a comparative study of information criteria focusing on MDIC ⋮ Variable selection strategies in survival models with multiple imputations ⋮ Estimating a difference of Kullback-Leibler risks using a normalized difference of AIC ⋮ Selection between proportional and stratified hazards models based on expected log-likelihood ⋮ Estimating the expectation of the Log-likelihood with censored data for estimator selection ⋮ Choice between Semi‐parametric Estimators of Markov and Non‐Markov Multi‐state Models from Coarsened Observations

Cites Work

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• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Generalised information criteria in model selection
• On Information and Sufficiency
• A new look at the statistical model identification

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