New insights on goodness-of-fit tests for ranked set samples

Ranked set sampling (RSS) utilizes auxiliary information on the variable of interest so as to assist the experimenter in acquiring an informative sample from the population. The resulting sample has a stratified structure, and often improves statistical inference with respect to the simple random sa...

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Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2022-12, Vol.63 (6), p.1777-1799
Main Authors: Mahdizadeh, M., Zamanzade, Ehsan
Format: Article
Language:English
Subjects:
Asymptotic properties
Distribution functions
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Fisheries
Goodness of fit
Hypotheses
Insurance
Management
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Population (statistical)
Probability Theory and Stochastic Processes
Random variables
Regular Article
Statistical inference
Statistical tests
Statistics
Statistics for Business
Test procedures
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Description
Summary:Ranked set sampling (RSS) utilizes auxiliary information on the variable of interest so as to assist the experimenter in acquiring an informative sample from the population. The resulting sample has a stratified structure, and often improves statistical inference with respect to the simple random sample of comparable size. In RSS literature, there are some goodness-of-fit tests based on the empirical estimators of the in-stratum cumulative distribution functions (CDFs). Motivated by the fact that the in-stratum CDFs in RSS can be expressed as functions of the population CDF, some new tests are developed and their asymptotic properties are explored. An extensive simulation study is performed to evaluate properties of different testing procedures when the parent distribution is normal. It turns out that the proposed tests can be considerably more powerful than their contenders in many situations. An application in the context of fishery is also provided.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-021-01284-7