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Kernel Regression Estimators for Nonparametric Model Calibration in Survey Sampling

  • N. G. Cadigan
  • J. Chen
Article

Abstract

This paper introduces new kernel regression estimators with strictly non-negative smoothing weights that are iteratively adjusted. One estimator shares the “optimal” asymptotic bias and variance of the local linear regressor. Other estimators have zero sum of residuals, a desirable property in many applications. In a survey sampling context these estimators can easily be adjusted so that they are internally bias calibrated, which is a property with intuitive appeal. We demonstrate in simulations that one of the estimators with zero sum residuals has bias and variance properties that are very close to “optimal”. In addition, we propose a potentially useful refinement to the usual orders of asymptotic approximations for bias and variance of kernel regression smoothers. The smoothers are illustrated using two examples from fisheries applications, one of which involves data from a stratified random bottom-trawl survey.

AMS Subject Classification

62G08 62G20 

Keywords

Kernel smoothing Residuals Nonparametric regression Nadaraya-Watson estimator Mack-Müller estimator Local linear estimator 

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Copyright information

© Grace Scientific Publishing 2010

Authors and Affiliations

  1. 1.Science Branch, Fisheries and Oceans CanadaNorthwest Atlantic Fisheries CenterSt. John’sCanada
  2. 2.University of British ColumbiaVancouverCanada

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