Comput Stat DOI 10.1007/s00180-016-0698-7 ORIGINAL PAPER Tests of perfect judgment ranking using pseudo-samples Saeid Amiri 1 · Reza Modarres 2 · Silvelyn Zwanzig 3 Received: 15 December 2015 / Accepted: 7 October 2016 © Springer-Verlag Berlin Heidelberg 2016 Abstract Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some infer- ential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set. Keywords Imperfect rankings · Order statistics · Ranked set sampling · Resampling 1 Introduction The past three decades have witnessed considerable research and success to extend the seminal idea of McIntyre (1952) to collect a sample only after judgment ranking of the sampling units. RSS is a two-stage sampling plan where a number of sam- pling units are first ranked without taking actual measurements at a small cost and, B Saeid Amiri saeid.amiri1@gmail.com 1 Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI, USA 2 Department of Statistics, The George Washington University, Washington, DC, USA 3 Department of Mathematics, Uppsala University, Uppsala, Sweden 123