Efficient inference about the tail weight in multivariate Student t distributions

Christophe Ley*, Anouk Neven

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)123-134
Number of pages12
JournalJournal of Statistical Planning and Inference
Publication statusPublished - 1 Dec 2015
Externally publishedYes


  • Efficient testing procedures
  • Likelihood ratio test
  • Local asymptotic normality
  • Student t distribution
  • Tail weight


Dive into the research topics of 'Efficient inference about the tail weight in multivariate Student t distributions'. Together they form a unique fingerprint.

Cite this