Information complexity based modeling in the presence of length-biased sampling

DOI10.1080/15598608.2010.10411972zbMath1216.62007OpenAlexW1969333566MaRDI QIDQ538222

Olcay Akman

Publication date: 24 May 2011

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2010.10411972

zbMATH Keywords

mixture modelssampling distributioninverse Gaussian

Mathematics Subject Classification ID

Point estimation (62F10) Statistical aspects of information-theoretic topics (62B10)

Cites Work

• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Likelihood of a model and information criteria
• Informational complexity criteria for regression models.
• On the reliability studies of a weighted inverse Gaussian model
• Akaike's information criterion and recent developments in information complexity
• On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models
• A Bayesian extension of the minimum AIC procedure of autoregressive model fitting
• A new look at the statistical model identification
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