Quantile inference based on clustered data

Omer Ozturk () and Asuman Turkmen ()
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Omer Ozturk: The Ohio State University
Asuman Turkmen: The Ohio State University

Metrika: International Journal for Theoretical and Applied Statistics, 2016, vol. 79, issue 7, No 5, 867-893

Abstract: Abstract One-sample sign test is one of the common procedures to develop distribution-free inference for a quantile of a population. A basic requirement of this test is that the observations in a sample must be independent. This assumption is violated in certain settings, such as clustered data, grouped data and longitudinal studies. Failure to account for dependence structure leads to erroneous statistical inferences. In this study, we have developed statistical inference for a population quantile of order p in either balanced or unbalanced designs by incorporating dependence structure when the distribution of within-cluster observations is exchangeable. We provide a point estimate, develop a testing procedure and construct confidence intervals for a population quantile of order p. Simulation studies are performed to demonstrate that the confidence intervals achieve their nominal coverage probabilities. We finally apply the proposed procedure to Academic Performance Index data.

Keywords: Sign test; Quantile estimation; Academic Performance Index data (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00184-016-0581-0

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