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Journal ArticleDOI

An information criterion for parameters under a simple order restriction

Kazuo Anraku
- 01 Mar 1999 - 
- Vol. 86, Iss: 1, pp 141-152
TLDR
In this article, an information criterion is proposed for detecting the configuration of the true parameters with the simple order restriction, which can also be applied for detecting a changepoint in a sequence of parameters with a monotone trend.
Abstract
Suppose we have independent random samples from each of k populations specified by scalar-valued, unknown parameters θ 1 ,...,θ k satisfying the simple order restriction θ 1 ≤...≤ θ k . Our concern is to seek distinct parameters among θ 1 θ k based on the data. To find a configuration of distinct parameters among the 0's, one may consider employing Akaike's information criterion (Akaike, 1973). However, the criterion is not appropriate for the order-restricted maximum likelihood estimator of θ = (θ 1 ,..., θ k ), since the normality or the asymptotic normality of the estimator is not valid. In this paper an information criterion is proposed for detecting the configuration of the true parameters with the simple order restriction. This method may also be applied for detecting a changepoint in a sequence of parameters with a monotone trend. A Monte Carlo study indicates that our new criterion is effective, compared to Akaike's information criterion, for detecting the configuration of normal means satisfying the simple order restriction.

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Citations
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Journal ArticleDOI

Inequality Constrained Analysis of Variance: A Bayesian Approach.

TL;DR: In this article, a Bayesian approach to evaluate analysis of variance or analysis of covariance models with inequality constraints on the (adjusted) means is presented and contains two issues: estimation of the parameters given the restrictions using the Gibbs sampler and model selection using Bayes factors in the case of competing theories.
BookDOI

Dose Finding in Drug Development

TL;DR: The book introduces the drug development process, the design and the analysis of clinical trials, and important procedural steps from a pharmaceutical industry perspective.
Journal ArticleDOI

Bayesian model selection of informative hypotheses for repeated measurements

TL;DR: In this paper, the Bayes factor is used to determine which hypothesis receives most support from the data, which is a pivotal element in the Bayesian framework is the specification of the prior, and training data in combination with restrictions on the measurement means are used to obtain so-called constrained posterior priors.
References
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Proceedings Article

Information Theory and an Extention of the Maximum Likelihood Principle

H. Akaike
TL;DR: 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 to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI

Information Theory and an Extension of the Maximum Likelihood Principle

TL;DR: 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.
Journal ArticleDOI

A test for differences between treatment means when several dose levels are compared with a zero dose control.

Williams Da
- 01 Mar 1971 - 
TL;DR: In this paper, the authors consider the case of a single quantitative variate and assume that the response, if any, of the variate to the substance is a change in the mean.
Journal ArticleDOI

Generalised information criteria in model selection

TL;DR: In this article, the authors investigated the problem of evaluating the goodness of statistical models from an information-theoretic point of view and proposed information criteria for evaluating models constructed by various estimation procedures when the specified family of probability distributions does not contain the distribution generating the data.