Convergence rates of a class of multivariate density estimators based on adaptive partitioning

Convergence rates of a class of multivariate density estimators based on adaptive partitioning

Density estimation is a fundamental problem in statistics. It is a building block for other statistical methods, such as classification, nonparametric testing, and data compression. In this dissertation, I focus on a non-parametric approach to multivarite density estimation, and study the asymptotic...

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Bibliographic Details
Main Author: Liu, Linxi
Corporate Author: Stanford University Department of Statistics
Other Authors: Candès, Emmanuel J (Emmanuel Jean) (Thesis advisor, advisor.), Diaconis, Persi (Thesis advisor, advisor), Wong, Wing Hung (Thesis advisor, primary advisor)
Format: Thesis Electronic Book
Language:English
Published: 2016

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Stanford University

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Call Number: 3781 2016 L
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