Nonparametric tests for the median from a size-biased sample
Authors:
Qing Kang a;
Paul I. Nelson b
| Affiliations: | a Department of Statistics, North Dakota State University, Fargo, ND, USA |
| b Department of Statistics, Kansas State University, Manhattan, KS, USA |
DOI:
10.1080/10485250701830113
Publication Frequency:
8 issues per year
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
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Abstract
This study explores issues related to one-sample nonparametric tests for the median of a continuous distribution when the sample is collected via size-bias of a known order. A general principle on how to construct the reference distribution of a given test statistic is presented. Following this principle, we create new bias-corrected nonparametric testing procedures. Computationally intensive, exact P-values are available for a small sample. When the sample size is large, P-values can be easily estimated by the asymptotic approximation developed here. Power functions of these tests are investigated in both small- and large-sample cases and consistency is shown to hold under fairly general conditions. The tests' performances are then compared via asymptotic relative efficiency under four theoretical distributions.
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| Keywords: nonparametric test; permutation test; testing median; size-biased sample; weighted distribution; asymptotic relative efficiency |
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