Article (Scientific journals)
Efficient inference about the tail weight in multivariate Student t distributions
Neven, Anouk; Ley, Christophe
2015In Journal of Statistical Planning and Inference
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Abstract :
[en] We propose a new testing procedure about the tail weight parameter of multivariate Student t distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the classical likelihood ratio test, but outperforms the latter by its flexibility and simplicity: indeed, our approach allows to estimate the location and scatter nuisance parameters by any root-n consistent estimators, hereby avoiding numerically complex maximum likelihood estimation. The finite-sample properties of our test are analyzed in a Monte Carlo simulation study, and we apply our method on a financial data set. We conclude the paper by indicating how to use this framework for efficient point estimation. Keywords and Phrases: efficient testing procedures; likelihood ratio test; local asymptotic normality; Student t distribution; tail weight
Disciplines :
Mathematics
Author, co-author :
Neven, Anouk ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Mathematics Research Unit
Ley, Christophe
External co-authors :
yes
Language :
English
Title :
Efficient inference about the tail weight in multivariate Student t distributions
Publication date :
2015
Journal title :
Journal of Statistical Planning and Inference
ISSN :
0378-3758
Publisher :
Elsevier Science
Peer reviewed :
Peer Reviewed verified by ORBi
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since 16 November 2017

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