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2024 | OriginalPaper | Buchkapitel

Minimum Kernel Discrepancy Estimators

verfasst von : Chris J. Oates

Erschienen in: Monte Carlo and Quasi-Monte Carlo Methods

Verlag: Springer International Publishing

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Abstract

For two decades, reproducing kernels and their associated discrepancies have facilitated elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now receiving interest in statistics and related fields, as criteria that can be used to select an appropriate statistical model for a given dataset. The focus of this article is on minimum kernel discrepancy estimators, whose use in statistical applications is reviewed, and a general theoretical framework for establishing their asymptotic properties is presented.

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Vorheriges Kapitel Optimal Algorithms for Numerical Integration: Recent Results and Open Problems
Nächstes Kapitel Error Estimates and Variance Reduction for Nonequilibrium Stochastic Dynamics
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Fußnoten
1
i.e. additional levels of differentiability in directions that are axis-aligned.
 
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Metadaten
Titel
Minimum Kernel Discrepancy Estimators
verfasst von
Chris J. Oates
Copyright-Jahr
2024
Buch
Monte Carlo and Quasi-Monte Carlo Methods

Print ISBN: 978-3-031-59761-9

Electronic ISBN: 978-3-031-59762-6

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-59762-6_6

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