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Bayesian information criterion

About: Bayesian information criterion is a(n) research topic. Over the lifetime, 3548 publication(s) have been published within this topic receiving 257758 citation(s). The topic is also known as: Schwarz criterion. more


Open accessJournal ArticleDOI: 10.1109/TAC.1974.1100705
Abstract: The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples. more

Topics: Likelihood function (61%), Akaike information criterion (61%), Statistical model (60%) more

42,619 Citations

Open accessJournal ArticleDOI: 10.1214/AOS/1176344136
Abstract: The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution. more

Topics: Bayesian information criterion (57%), g-prior (55%), Bayes' theorem (55%) more

35,659 Citations

Book ChapterDOI: 10.1007/978-1-4612-1694-0_15
01 Jan 1973-
Abstract: In this paper 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 provide answers to many practical problems of statistical model fitting. more

Topics: Likelihood principle (64%), Likelihood function (59%), Bayesian information criterion (58%) more

15,032 Citations

Open accessJournal ArticleDOI: 10.1093/MOLBEV/MSN083
David Posada1Institutions (1)
Abstract: jModelTest is a new program for the statistical selection of models of nucleotide substitution based on "Phyml" (Guindon and Gascuel 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 52:696-704.). It implements 5 different selection strategies, including "hierarchical and dynamical likelihood ratio tests," the "Akaike information criterion," the "Bayesian information criterion," and a "decision-theoretic performance-based" approach. This program also calculates the relative importance and model-averaged estimates of substitution parameters, including a model-averaged estimate of the phylogeny. jModelTest is written in Java and runs under Mac OSX, Windows, and Unix systems with a Java Runtime Environment installed. The program, including documentation, can be freely downloaded from the software section at more

9,080 Citations

Journal ArticleDOI: 10.1177/0049124104268644
Abstract: The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information... more

7,541 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Paul D. McNicholas

22 papers, 952 citations

Stan E. Dosso

14 papers, 261 citations

Joseph E. Cavanaugh

12 papers, 444 citations

Małgorzata Bogdan

10 papers, 298 citations

Chih-Ling Tsai

9 papers, 366 citations

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