 Maximum likelihood estimation of the mean of a multivariate normal population with monotone incomplete dataMegan M. Romer and Donald St. P. Richards Statistics & Probability Letters, 2010, vol. 80, issue 17-18, 1284-1288 Abstract: Given a two-step, monotone incomplete, random sample from , a multivariate normal population with mean and covariance matrix , we consider the problem of deriving an exact stochastic representation for , the maximum likelihood estimator of . We prove that and , the maximum likelihood estimators of and , respectively, are equivariant under a certain group of affine transformations, and then we apply the equivariance property to obtain a new derivation of a stochastic representation for established by Chang and Richards (2009). The new derivation induces explicit representations, in terms of the data, for the independent random variables that arise in the stochastic representation for . Keywords: Equivariance; Missing; data; Monotone; incomplete; data; Multivariate; normal; distribution; Stochastic; representation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:80:y:2010:i:17-18:p:1284-1288 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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