Modern likelihood inference for the maximum/minimum of a bivariate normal vector. Issue 10 (2nd July 2016)

• Record Type: Journal Article
• Authors: Mameli, V.; Brazzale, A. R.
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 10(2016)
• Page Start: 1869
• Page End: 1890
• Publication Date: 2016-07-02
• Subjects: Bivariate normal distribution -- higher order likelihood inference -- modified likelihood root -- skew-normal distribution; 62E20 -- 62F05 -- 62F25; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2015.1089872
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: We consider the use of modern likelihood asymptotics in the construction of confidence intervals for the parameter which determines the skewness of the distribution of the maximum/minimum of an exchangeable bivariate normal random vector. Simulation studies were conducted to investigate the accuracy of the proposed methods and to compare them to available alternatives. Accuracy is evaluated in terms of both coverage probability and expected length of the interval. We furthermore illustrate the suitability of our proposals by means of two data sets, consisting of, respectively, measurements taken on the brains of 10 mono-zygotic twins and measurements of mineral content of bones in the dominant and non-dominant arms for 25 elderly women.