Information Theory and an Extension of the Maximum Likelihood Principle

Key takeaways

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Bibliography: Akaike, H., 1998. Information Theory and an Extension of the Maximum Likelihood Principle, in: Parzen, E., Tanabe, K., Kitagawa, G. (Eds.), Selected Papers of Hirotugu Akaike, Springer Series in Statistics. Springer New York, New York, NY, pp. 199–213. https://doi.org/10.1007/978-1-4612-1694-0_15

Authors:: Emanuel Parzen, Kunio Tanabe, Genshiro Kitagawa, Hirotogu Akaike

Collections:: Methods, Missing Data Sim Paper

First-page: 199

Abstract

Abstract In this paper it is shown that the classical maximum likelihood principle cInanthibsepacponersiidt eisresdhotwonbtehaat tmheecthlaosdsicoafl masayxmimptuomticlikreelailhiozaotdiopnrinocfipalne coapntimbeumcoensstiidmearteedwtiothbreesapemctettohoadveorfy agseynmerpatloitnicforremaalitzioantiotnheoorfetainc corpitteimriounm. Teshtiismoabtesewrvitahtiroenspsehcotwtos aanveexrytegnesnioenraol finthfoerpmrainticoipnlethteoorpertoiccvriditeerainosnw. Terhsistoombsaenrvyaptiroanctischaolwpsroabnleemxtsenosfisotnatiosftitchael pmroindceilplfeitttiongp. rovide answers to many practical problems of statistical model fitting.

Citations

content: "@akaikeInformationTheoryExtension1998" -file:@akaikeInformationTheoryExtension1998

Reading notes

Imported on 2025-04-27 17:48

⭐ Important

& aper it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion. This observation shows an extension of the principle to p (p. 199)