PCA is used to orthogonalize correlated auxiliary variables for constructing a more efficient estimator of the finite population mean under simple random sampling, with derived bias and MSE showing improved performance in simulations.
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Principal Component Based Estimation of Finite Population Mean under Multicollinearity
PCA is used to orthogonalize correlated auxiliary variables for constructing a more efficient estimator of the finite population mean under simple random sampling, with derived bias and MSE showing improved performance in simulations.