Modern likelihood inference for the maximum/minimum of a bivariate normal vector

被引：1

作者：

Mameli, V. ^{[1
]}

Brazzale, A. R. ^{[2
]}

机构：

[1] Ca Foscari Univ Venice, Dept Environm Sci Informat & Stat, 155 Via Torino, Venice, Italy

[2] Univ Padua, Dept Stat Sci, 241 Via Cesare Battisti, Padua, Italy

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2016年 / 86卷 / 10期

关键词：

modified likelihood root; skew-normal distribution; higher order likelihood inference; Bivariate normal distribution; 62F25; 62E20; 62F05; RIGHT STATISTICAL MEASURE; TAIL PROBABILITIES; LINEAR FUNCTIONS; SKEW; DISTRIBUTIONS; CONCOMITANTS; FORMULA;

D O I：

10.1080/00949655.2015.1089872

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1869 / 1890

页数：22

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