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This paper describes an algorithm to improve a computational cost for estimation using the Kriging method with a large number of sampling data. An improved formula to compute the weighting coefficient for Kriging estimation is proposed. The Sherman–Morrison–Woodbury formula is applied to solving an approximated simultaneous equation to determine a weighting(More)
We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances where the number of controls may be much larger than the sample size. To make informative inference feasible, we require(More)
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