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Titlebook: Nonparametric Functional Estimation and Related Topics; George Roussas Book 1991 Springer Science+Business Media Dordrecht 1991 Estimator.

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书目名称Nonparametric Functional Estimation and Related Topics
编辑George Roussas
视频videohttp://file.papertrans.cn/668/667823/667823.mp4
丛书名称Nato Science Series C:
图书封面Titlebook: Nonparametric Functional Estimation and Related Topics;  George Roussas Book 1991 Springer Science+Business Media Dordrecht 1991 Estimator.
描述About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engine
出版日期Book 1991
关键词Estimator; Likelihood; Regression analysis; Time series; best fit; classification; linear regression
版次1
doihttps://doi.org/10.1007/978-94-011-3222-0
isbn_softcover978-94-010-5420-1
isbn_ebook978-94-011-3222-0Series ISSN 1389-2185
issn_series 1389-2185
copyrightSpringer Science+Business Media Dordrecht 1991
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Reproducing Kernels and Finite Order Kernelss. We define here a multi-stage procedure to build estimates based on increasing order kernels and leading to a data-driven choice of both the order and the smoothing parameter. Some asymptotic as well as non-asymptotic results are provided for the estimates.
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Exponential Inequalities in Nonparametric Estimationv statistic, the total variation distance for empirical measures, the Vapnik-Chervonenkis distance, and various performance criteria in nonparametric density estimation. We also derive bounds for the variances of these quantities.
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Nonparametric Estimation of Elliptically Contoured Densitiese analyze the large sample behavior of a kernel-type estimator of f, when both the parametric component (μ.) as well as the nonparametric transfer function k are unknown. It turns out that the rate of convergence is independent of d.
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Laws of the Iterated Logarithm for Density EstimatorsWe obtain laws of the iterated logarithm for density estimates. Our results are based on functional laws of the iterated logarithm for the increments of empirical processes due to Deheuvels and Mason (1990).
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On Asymptotic Efficiency of Average Derivative EstimatesIt is shown, using results of Koshevnik and Levit (1976), that the average derivative estimator of Haerdle and Stoker (1989) is the asymptotically efficient nonparametric estimator of the average derivative of regression function.
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