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Titlebook: Statistical Theory and Computational Aspects of Smoothing; Proceedings of the C Wolfgang Härdle,Michael G. Schimek Conference proceedings 1

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楼主: 削木头
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Smoothing by Local Regression: Principles and Methods,19th century and the early 20th century. Still, new work in local regression continues at a rapid pace. We review the history of local regression. We discuss four of its basic components that must be chosen in using local regression in practice — the weight function, the parametric family that is fi
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Variance Properties of Local Polynomials and Ensuing Modifications,eatures are in partial contradiction to variance properties for random design and to practical experience. The conditional variance is unbounded. The unconditional variance is infinite when using optimal (compact) weights. A tutorial illustration of construction of weights for kernel and local polyn
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Comments,be extended to other estimators, due to renewed interest for local polynomial fitting starting in the early 1990’s. Let us congratulate Marron, Cleveland and Loader, Seifert and Gasser for providing pertinent arguments on this question and valuable contributions to the study of this problem.
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Rejoinder, We indeed appreciate the open scientific atmosphere seen in all contributions. Some major topics have emerged from papers and discussions, and for a number of problems a consensus is in reach, within some margin of tolerance.
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A Note on Cross-Validation for Smoothing Splines,Robinson and Moyeed, 1989) are compared in a simulation study. It turns out that robustified cross-validation performs better. Computational problems of finding the cross-validation score are discussed. Findings from linearly transformed data lead to a reduction in costs. In addition we consider pro
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