Small-sample one-sided testing in extreme value regression models

Silvia Ferrari () and Eliane Pinheiro

AStA Advances in Statistical Analysis, 2016, vol. 100, issue 1, 79-97

Abstract: We derive adjusted signed likelihood ratio statistics for a general class of extreme value regression models. The adjustments reduce the error in the standard normal approximation to the distribution of the signed likelihood ratio statistic. We use Monte Carlo simulations to compare the finite-sample performance of the different tests. Our simulations suggest that the signed likelihood ratio test tends to be liberal when the sample size is not large and that the adjustments are effective in shrinking the size distortion. Two real data applications are presented and discussed. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Extreme value regression; Gumbel distribution; Nonlinear models; Signed likelihood ratio test; Small-sample adjustments (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10182-015-0251-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:100:y:2016:i:1:p:79-97

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-015-0251-y

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().