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A Note on Balanced Sampling and Robust Genetic Predictors
Author(s) -
Tallis G. M.
Publication year - 1982
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710240706
Subject(s) - best linear unbiased prediction , estimator , mathematics , statistics , sampling (signal processing) , covariance , linear model , mean squared error , sampling design , context (archaeology) , inference , computer science , artificial intelligence , selection (genetic algorithm) , biology , population , paleontology , demography , filter (signal processing) , sociology , computer vision
The concept of balanced sampling is applied to prediction in finite samples using model based inference procedures. Necessary and sufficient conditions are derived for a general linear model with arbitrary covariance structure to yield the expansion estimator as the best linear unbiased predictor for the mean. The analysis is extended to produce a robust estimator for the mean squared error under balanced sampling and the results are discussed in the context of statistical genetics where appropriate sampling produces simple efficient and robust genetic predictors free from unnecessary genetic assumptions.