BIAS ROBUST ESTIMATION IN FINITE POPULATIONS USING NONPARAMETRIC CALIBRATION

A standard problem in sample survey inference is that of predicting the finite population total H of a function h(y) of a random variable Y. The model-based approach to this problem first defines a working model xi for Y and then predicts H by estimating its expectation under xi, conditional on the sample values of Y. This approach leads to biased predictions if xi is incorrect. We explore an automatic solution to this misspecification bias that uses nonparametric regression to define a robust (but inefficient) predictor of H, and then calibrates this predictor for its bias under xi. An application to prediction of the finite population distribution function of a population of Australian beef farms is presented.