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Al-Kandari Noriah M. (Kuwait University), Lahiri Partha (University of Maryland)
Prediction of a Function of Misclassified Binary Data
Statistics in Transition, 2016, vol. 17, nr 3, s. 429-447, rys., tab., bibliogr. s. 445-447
Słowa kluczowe
Dobór próby badawczej, Teoria statystyki
Selection of test methods, Theory of statistics
We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the mis-classification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a bias-corrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE). (original abstract)
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Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
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