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Multivariate Statistical Analysis: The Geometric Theoryстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 25 сентября 2013 г.
- Автор: Tyurin Yu N.
- Журнал: Theory of Probability and its Applications
- Том: 55
- Номер: 1
- Год издания: 2011
- Издательство: Society for Industrial and Applied Mathematics
- Местоположение издательства: United States
- Первая страница: 91
- Последняя страница: 109
- DOI: 10.1137/S0040585X97984620
- Аннотация: This paper presents a new geometric approach to the multivariate statistical analysis. In essence, this geometric theory replicates the classical one-dimensional theory. As a tool for this generalization, an extended version compared to the classical linear algebra was developed. In this version, squared matrices are used as scalar products of elements. In terms of this generalized linear algebra, general concepts of linear models and linear hypotheses are given; a matrix-valued least squares method is given, and its relation to the classical one is discussed; a general method for testing linear statistical hypotheses in a geometric, coordinate-free form is developed.
- Добавил в систему: Родионов Тимофей Викторович