Bias and variance reduction techniques for bootstrap information criteria

DOI10.1007/s10463-009-0237-1zbMath1422.62140OpenAlexW1977517056MaRDI QIDQ904082

Genshiro Kitagawa, Sadanori Konishi

Publication date: 15 January 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-009-0237-1

zbMATH Keywords

Kullback-Leibler informationvariance reductionAICinformation criteriabootstrappingstatistical functionalhigher-order bias correction

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric statistical resampling methods (62G09)

Related Items (2)

Permutation bootstrap and the block maxima method ⋮ Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units

Cites Work

• Bootstrap methods: another look at the jackknife
• Bootstrapping log likelihood and EIC, an extension of AIC
• Asymptotic theory for information criteria in model selection -- functional approach
• Information criteria and statistical modeling.
• A Note on the Modified Likelihood for Density Estimation
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• Approximate predictive likelihood
• Regression and time series model selection in small samples
• Bootstrap likelihoods
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Generalised information criteria in model selection
• On Information and Sufficiency
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Bias and variance reduction techniques for bootstrap information criteria