Maximum Likelihood and Multivariate Normal Distribution

Kohei Adachi ()
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Kohei Adachi: Osaka University, Graduate School of Human Sciences

Chapter Chapter 8 in Matrix-Based Introduction to Multivariate Data Analysis, 2016, pp 109-126 from Springer

Abstract: Abstract In the analysis procedures introduced in the last four chapters, parameters are estimated by the least squares (LS) methodLeast squares (LS) method , as reviewed in Sect. 8.1. The remaining sections in this chapter serve to prepare readers for the following chapters, in which a maximum likelihood (ML) methodMaximum likelihood (ML) method , which differs from LS, is used for estimating parameters. That is, the ML method is introduced in Sect. 8.2, which is followed by describing the notion of probability densityProbability density function and the ML method with multivariate normal distributionNormal distribution (Gaussian distribution) . Finally, ML-based modelModel selection with information criteria is introduced.

Date: 2016
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DOI: 10.1007/978-981-10-2341-5_8

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