Local polynomial regresssion estimators in survey sampling
نویسندگان
چکیده
منابع مشابه
Local Polynomial Regression Estimators in Survey Sampling
Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression estimators, these estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the superpopulation model are consid...
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A new class of model-assisted estimators based on local polynomial regression is suggested. The estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals. The es-timators are asymptotically design-unbiased and consistent under mild assumptions , and we provide a consistent estimator for the design mean squared error. Bandwidth se...
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We show that the model-calibration estimator for the finite population mean, which was proposed by Wu & Sitter (2001) through an intuitive argument, is optimal among a class of calibration estimators. We also present optimal calibration estimators for the finite population distribution function, the population variance, the variance of a linear estimator and other quadratic finite population fu...
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We generalize a method for proving uniform in bandwidth consistency results for kernel type estimators developed by the two last named authors. Such results are shown to be useful in establishing consistency of local polynomial estimators of the regression function.
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This paper addresses the choice of an optimal smoothing parameter for local polynomial matching. A version of Empirical Bias Bandwidth Selection (EBBS) proposed by Ruppert (1997) is applied to account for the MSE computation of the matching estimator. Thereby, an estimator for the large sample variance of the local polynomial matching estimator is also provided. A Monte Carlo study indicates be...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2000
ISSN: 0090-5364
DOI: 10.1214/aos/1015956706