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A Class of Efficient Non‐parametric K ‐sample Tests for Multivariate Counting Processes with Frailties
Author(s) -
Chang IShou,
Chen LiRu,
Hsiung Chao
Publication year - 1998
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/1467-9469.00093
Subject(s) - mathematics , parametric statistics , multivariate statistics , statistics , asymptotic analysis , class (philosophy) , parametric model , sample size determination , sample (material) , counting process , statistical hypothesis testing , artificial intelligence , computer science , chromatography , chemistry
A K ‐sample testing problem is studied for multivariate counting processes with time‐dependent frailty. Asymptotic distributions and efficiency of a class of non‐parametric test statistics are established for certain local alternatives. The concept of efficiency is to show that for every non‐parametric test in this class, there is a parametric submodel for which the optimal test has the same asymptotic power as the non‐parametric one. The theory is applied to analyse a diabetic retinopathy study data set. A simulation study is also presented to illustrate the theory