An equivalence between least squares and maximum likelihood estimation in the general linear model

Mahbuba Yeasmin, Maxwell Leslie King

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We show an equivalence between a particular form of least squares and standard maximum likelihood estimation in the general linear regression model using estimating equations. This means that the normality assumption is not crucial for maximum likelihood estimation in this model.
Original languageEnglish
Pages (from-to)13 - 16
Number of pages4
JournalGlobal Journal of Quantitative Science
Volume1
Issue number2
Publication statusPublished - 2014

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