An Algorithm for Computing the Nonparametric MLE of a Mixing Distribution
作者:
MaryL. Lesperance,
JohnD. Kalbfleisch,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 417
页码: 120-126
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475182
出版商: Taylor & Francis Group
关键词: Intra-simplex direction method;Maximum likelihood estimation;Mixture models;Profile likelihood;Vertex direction method;Vertex exchange method
数据来源: Taylor
摘要:
A fast algorithm for calculating the nonparametric maximum likelihood estimate (MLE) of a mixing distribution,G(·). in a mixture model is discussed. The literature contains several methods for computing the nonparametric MLE of G, but these methods are slow. In this article we develop an algorithm for maximizing the log-likelihoodl(G) over the family ℊ, of all distribution functions, that yields the nonparametric MLE ofG. In some semiparametric problems, a structural or fixed parameterβis of interest, and we are interested in computing profile likelihood, supGl(G, β), for a grid of values ofβ. The algorithm that we propose is fast enough for this purpose. Examples illustrate and compare the algorithms; one taken from an article by Laird, the common mean problem, and one taken from the literature on optimal experimental design. It is also noted that routines from the literature on semi-infinite programming may be used to compute the profile log-likelihood.
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