Small-sample likelihood inference in extreme-value regression models

Details Small-sample likelihood inference in extreme-value regression models Small-sample likelihood inference in extreme-value regression models

2012-04-18

arxiv.org/abs/1204.3949v1

Mathematics

Statistics Theory

18 pages, 5 figures

Scientific paper

We deal with a general class of extreme-value regression models introduced by Barreto- Souza and Vasconcellos (2011). Our goal is to derive an adjusted likelihood ratio statistic that is approximately distributed as \c{hi}2 with a high degree of accuracy. Although the adjusted statistic requires more computational effort than its unadjusted counterpart, it is shown that the adjustment term has a simple compact form that can be easily implemented in standard statistical software. Further, we compare the finite sample performance of the three classical tests (likelihood ratio, Wald, and score), the gradient test that has been recently proposed by Terrell (2002), and the adjusted likelihood ratio test obtained in this paper. Our simulations favor the latter. Applications of our results are presented. Key words: Extreme-value regression; Gradient test; Gumbel distribution; Likelihood ratio test; Nonlinear models; Score test; Small-sample adjustments; Wald test.

Affiliated with

Also associated with

No associations

Rating

Small-sample likelihood inference in extreme-value regression models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Small-sample likelihood inference in extreme-value regression models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Small-sample likelihood inference in extreme-value regression models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-410850