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PREDICTION OF THE FINITE POPULATION DISTRIBUTION FUNCTION UNDER GAUSSIAN SUPERPOPULATION MODELS
Author(s) -
Bolfarine Heleno,
Sandoval Mönica C.
Publication year - 1993
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1993.tb01325.x
Subject(s) - statistics , variance (accounting) , monte carlo method , mathematics , gaussian , population , sample (material) , sample size determination , econometrics , estimation , mathematical optimization , computer science , medicine , engineering , physics , chemistry , accounting , environmental health , chromatography , quantum mechanics , business , systems engineering
Summary This article considers optimal prediction of the finite population distribution function under Gaussian superpopulation models, which allows auxiliary prior information to be incorporated into the estimation process. Large sample approximations for the variance of the optimal predictors are derived in some special important cases. A small scale Monte Carlo study illustrates comparisons between the optimal predictor and some others which are proposed in the literature. The conclusion is that the optimal predictor can be considerably more efficient in situations where the normal superpopulation model is adequate.