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

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.Comment: 20 pages and 3 figure

Practical Small Sample Asymptotics for Distributions Used in Life-Data Analysis

Fraser (1968) and Lawless (1982, Appendix G) discussed exact conditional intervals for the parameters and quantiles of the location-scale model when complete data are used. Moreover, Lawless (1982) extended the exact method to failure-censored (Type II censored) data. Nevertheless, the exact intervals are difficult to obtain in practice and are unavailable under time censoring (Type I censoring). As a consequence, approximate large-sample intervals are widely used. In this article, a likelihood based third order procedure is developed. The method does not require explicit nuisance parameterization and can be easily implemented into algebraic computational packages. Numerical examples are presented to show the accuracy of the method even when the sample size is small. Keywords: Ancillary; Bartlett correction; Likelihood ratio statistic; Mean and variance correction. 1 1. INTRODUCTION The location-scale model has density of the form f(y; ; oe) = 1 oe g ` y \Gamma oe ' \Gamma 1 !..