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Erschienen in:
2020 | OriginalPaper | Buchkapitel
8. Maximum Likelihood and Multivariate Normal Distribution
verfasst von : Kohei Adachi
Erschienen in: Matrix-Based Introduction to Multivariate Data Analysis
Verlag: Springer Singapore
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Abstract
In the analysis procedures introduced in the last four chapters, parameters 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 parameters. That is, the ML method is introduced in Sect. 8.2, which is followed by describing the notion of probability density function and the ML method with multivariate normal distribution. Finally, ML-based model selection with information criteria is introduced.