Nonparametric Bootstrap Estimation of the Population Ratio Using Ranked Set Sampling

Year: 2012       Vol.: 61       No.: 2      

Authors: Kevin Carl P. Santos; Charisse Mae I. Castillo; Reyna Belle d.S. de Jesus; Nina B. Telan; Crystal Angela P. Vidal

Abstract:

Ranked Set Sampling (RSS) yields unbiased and more reliable estimators of the population mean and proportion while keeping low costs. Using nonparametric bootstrap estimation, the efficiency of the ratio estimates using RSS with Simple Random Sampling (SRS) are compared. A simulation study accounting for the sampling rate, population size, population variance and correlation with the concomitant variable was conducted to compare RSS and SRS in estimating ratios. When ranking was done on the numerator characteristic, RSS generally performs better than SRS in terms of their relative bias. Likewise, in terms of precision, RSS generally produces better estimates when ranking was done on the numerator characteristic. On homogeneous populations, contrary to what was expected, RSS performed better over SRS. On heterogeneous populations, on the other hand, the two sampling designs are generally comparable

Keywords: population ratio; ranked set sampling; simple random sampling; nonparametric bootstrap estimation

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