Random Dimension Low Sample Size Asymptotics

Авторы: Christoph Gerd, Ulyanov Vladimir V.
Сборник: Recent Developments in Stochastic Methods and Applications
Серия: Springer Proceedings in Mathematics & Statistics
Том: 371
Год издания: 2021
Издательство: Springer International Publishing AG
Местоположение издательства: Cham, Switzerland
Первая страница: 215
Последняя страница: 228
DOI: 10.1007/978-3-030-83266-7_16
Аннотация: A first investigation of high-dimensional low-sample-size (HDLSS) asymptotics, Hall, Marron and Neeman (2005) discovered a surprisingly rigid geometric structure. A sample of size k taken from the standard m-dimensional normal distribution is for large m close to the vertices of the k-dimensional simplex in m-dimensional vector space. It follows from the analysis of three geometric statistics: the length of an observation, the distance between any two independent observations and the angle between these vectors. We generalize and refine the results constructing the second order Chebyshev-Edgeworth expansions under assumption that the data dimension is random and different scaling factors are chosen.
Добавил в систему: Ульянов Владимир Васильевич

	ИСТИНА	Войти в систему Регистрация
	Интеллектуальная Система Тематического Исследования НАукометрических данных
	Главная Поиск Статистика О проекте Помощь

ИСТИНА