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Resampling Unbalanced Ranked Set Samples With Applications in Testing Hypothesis About the Population Mean

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Abstract

Ranked set sampling is a sampling approach that could lead to improved statistical inference when the actual measurement of the variable of interest is difficult or expensive to obtain but sampling units can be easily ordered by some means without actual quantification. In this paper, we consider the problem of bootstrapping an unbalanced ranked set sample (URSS) where the number of observations from each artificially created stratum can be unequal. We discuss resampling a URSS through transforming it into a balanced RSS and extending the existing algorithms. We propose two methods that are designed to obtain resamples from the given URSS. Algorithms are provided and several properties, including asymptotic normality of estimates, are discussed. The proposed methods are compared with the parametric bootstrap using Monte Carlo simulations for the problem of testing a hypothesis about the population mean.

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Author information

Authors and Affiliations

  1. Devision of Biostatistics, Department of Epidemiology and Public Health, University of Miami, Miami, USA

    Saeid Amiri

  2. Department of Statistics, University of Manitoba, Winnipeg, MB, Canada, R3T 2N2

    Mohammad Jafari Jozani

  3. Department of Statistics, The George Washington University, Washington, DC, USA

    Reza Modarres

Authors
  1. Saeid Amiri
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  2. Mohammad Jafari Jozani
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  3. Reza Modarres
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Correspondence to Saeid Amiri.

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Amiri, S., Jafari Jozani, M. & Modarres, R. Resampling Unbalanced Ranked Set Samples With Applications in Testing Hypothesis About the Population Mean. JABES 19, 1–17 (2014). https://doi.org/10.1007/s13253-013-0153-y

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  • DOI: https://doi.org/10.1007/s13253-013-0153-y

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