Distribution-Free Tests for Two-Sample Location Problems Based on Subsamples | Zendy

Deepa R. Acharya | Zendy; Parameshwar V. Pandit | Zendy

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Distribution-Free Tests for Two-Sample Location Problems Based on Subsamples

Author(s) -

Deepa R. Acharya,

Parameshwar V. Pandit

Publication year - 2016

Publication title -

journal of advanced statistics

Language(s) - English

Resource type - Journals

eISSN - 2414-6811

pISSN - 2414-6803

DOI - 10.22606/jas.2016.11004

Subject(s) - nonparametric statistics , mathematics , statistics , statistical hypothesis testing , kernel (algebra) , class (philosophy) , sample (material) , location parameter , kernel density estimation , maxima and minima , asymptotic distribution , distribution (mathematics) , sample size determination , probability distribution , computer science , combinatorics , artificial intelligence , mathematical analysis , chemistry , chromatography , estimator

Nonparametric tests for location problems have received much attention in the literature. Many nonparametric tests have been proposed for one, two and several samples location problems. In this paper a class of test statistics is proposed for two sample location problem when the underlying distributions of the samples are symmetric. The class of test statistics proposed is linear combination of U-statistics whose kernel is based on subsamples extrema. The members of the new class are shown to be asymptotically normal. The performance of the proposed class of tests is evaluated using Pitman Asymptotic Relative Efficiency. It is observed that the members of the proposed class of tests are better than the existing tests in the literature.

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