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Maximum Likelihood and Multivariate Normal Distribution

In: Matrix-Based Introduction to Multivariate Data Analysis

Author

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  • Kohei Adachi

    (Osaka University, Graduate School of Human Sciences)

Abstract

InMultivariate normal (MVN) distribution the analysis procedures introduced in the last four chapters, parametersParameter are estimated by the least 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) method, which differs from LS, is used for estimating parametersParameter . That is, the ML methodMaximum likelihood (ML) method is introduced in Sect. 8.2, which is followed by describing the notion of probabilityProbability density function (PDF) density function and the ML methodMaximum likelihood (ML) method with multivariate normal distributionMultivariate normal (MVN) distribution . Finally, ML-based modelModel selection selectionModel selection with information criteria is introduced.

Suggested Citation

  • Kohei Adachi, 2020. "Maximum Likelihood and Multivariate Normal Distribution," Springer Books, in: Matrix-Based Introduction to Multivariate Data Analysis, edition 2, chapter 0, pages 111-130, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-4103-2_8
    DOI: 10.1007/978-981-15-4103-2_8
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