NDLI: A strong large deviation theorem

Content Provider	Springer Nature Link
Author	Joutard, C.
Copyright Year	2013
Abstract	We prove a strong large deviation theorem for an arbitrary sequence of random variables, that is, we establish a full asymptotic expansion of large deviation type for the tail probabilities. An Edgeworth expansion is required to derive the result. We illustrate our theorem with two statistical applications: the sample variance and the kernel density estimator.
Starting Page	155
Ending Page	164
Page Count	10
File Format	PDF
ISSN	10665307
Journal	Mathematical Methods of Statistics
Volume Number	22
Issue Number	2
e-ISSN	19348045
Language	English
Publisher	Allerton Press, Inc.
Publisher Date	2013-06-27
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	strong large deviations Edgeworth expansion kernel density estimator Inequalities; stochastic orderings Density estimation Nonparametric regression Statistical Theory and Methods
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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A strong large deviation theorem