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The Relative Efficiency of Ranked Set Sampling in Ordinary Least Squares Regression

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Abstract

Ranked set sampling was developed for situations where measurement cost is expensive compared with unit acquisition. This paper presents results of simulations and theory examining the impact of balanced ranked set sampling on the relative efficiencies of the slope and intercept estimators of an ordinary least squares regression. Perfect ranking of either the independent or the dependent variable is assumed throughout. In contradistinction to most of the published ranked set sampling work, it is demonstrated that balanced ranked set sampling offers at most little improvement in the relative efficiencies of the slope estimator at any sample size.

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Authors and Affiliations

  1. Department of Accounting and Information Systems, College of Business and Public Administration, Eastern Washington University, 668 N. Riverpoint Blvd. Suite A, Spokane, Washington, 99202-1660, USA

    Elizabeth J. Tipton Murff

  2. Department of Management Science and Information Systems, McCombs School of Business, The University of Texas at Austin, Austin, Texas, 78712-1178, USA

    Thomas W. Sager

Authors
  1. Elizabeth J. Tipton Murff
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  2. Thomas W. Sager
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Correspondence to Elizabeth J. Tipton Murff.

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Murff, E.J.T., Sager, T.W. The Relative Efficiency of Ranked Set Sampling in Ordinary Least Squares Regression. Environ Ecol Stat 13, 41–51 (2006). https://doi.org/10.1007/s10651-005-5689-8

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