Multiplicative Bias Corrected Nonparametric Smoothers
| Autor: | Thomas Burr, Eric Matzner-Løber, Laurent Rouvière, Nicolas W. Hengartner |
|---|---|
| Přispěvatelé: | Theorical Division (LANL), Los Alamos National Laboratory (LANL), Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Rennes 2 (UR2), Centre de Recherche en Économie et Statistique (CREST), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)-École polytechnique (X)-École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris)-Centre National de la Recherche Scientifique (CNRS), ANR-09-BLAN-0051,CLARA,Classification non-supervisée en grande dimension : algorithmes et applications(2009) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
05 social sciences
Multiplicative function Nonparametric statistics Local regression Estimator Context (language use) Nonparametric regression [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] local linear estimate 01 natural sciences 010104 statistics & probability Delta method Standard error [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] bias reduction 0502 economics and business Applied mathematics 0101 mathematics 050205 econometrics Mathematics |
| Zdroj: | Nonparametric statistics, Springer proceedings in Mathematics and Statistics, 3rd ISNPS, Avignon, 2016. Nonparametric statistics, Springer proceedings in Mathematics and Statistics, 3rd ISNPS, Avignon, 2016., 2018, ⟨10.1007/978-3-319-96941-1_3⟩ Springer Proceedings in Mathematics & Statistics ISBN: 9783319969404 |
| DOI: | 10.1007/978-3-319-96941-1_3⟩ |
| Popis: | This contribution presents a general multiplicative bias reduction strategy for nonparametric regression. The approach is most effective when applied to an oversmooth pilot estimator, for which the bias dominates the standard error. The practical usefulness of the method was demonstrated in Burr et al. (IEEE Trans Nucl Sci 57:2831–2840, 2010) in the context of estimating energy spectra. For such data sets, it was observed that the method could decrease significantly the bias with only negligible increase in variance. This chapter presents the theoretical analysis of that estimator. In particular, we study the asymptotic properties of the bias corrected local linear regression smoother, and prove that it has zero asymptotic bias and the same asymptotic variance as the local linear smoother with a suitably adjusted bandwidth. Simulations show that our asymptotic results are available for modest sample sizes. |
| Databáze: | OpenAIRE |
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