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Maximum Likelihood and Multivariate Normal Distribution

Authors :
Kohei Adachi
Source :
Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9789811023408, Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9789811541025
Publication Year :
2016
Publisher :
Springer Singapore, 2016.

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.

Details

ISBN :
978-981-10-2340-8
ISBNs :
9789811023408
Database :
OpenAIRE
Journal :
Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9789811023408, Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9789811541025
Accession number :
edsair.doi.dedup.....a215fca9a0a4b89ebcaff8b26ef57d61
Full Text :
https://doi.org/10.1007/978-981-10-2341-5_8