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A distribution-free theory of nonparametric regression. Springer
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Linton and E. Linton and J. Nielsen , A kernel method of estimating structured nonparametric regression based on marginal integration , Biometrika , vol. Linton and B. Lu and O.
Mammen, O. There is a useful mathematical appendix with proofs and exponential type inequalities for sums of independent variables and for sum of martingale differences. Each chapter has a section called "Bibliographic Notes" containing references to the extensive bibliography of more than items. A must have book.
This book is excellent as a reference, because the proofs are written in an extremely clear manner and the topics selected are discussed very clearly and are interesting. Prewitt, Journal of the American Statistical Association, Because of the clear mathematical presentation it can be used also for a course on nonparametric regression estimation. It is clearly written, and presents a wealth of popular statistical methods relevant in many application areas.
Hence, it will be an often consulted book in the academic library, and a good source on which to build a lecture course. Coolen, Kwantitatieve Methoden, Issue 70B38, The book follows the style Theorem-Proof and gives rigorous derivations of all the results. There is a useful mathematical appendix with proofs of exponential type inequalities for sums of independent variables and for sums of martingale differences.
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A Distribution-Free Theory of Nonparametric Regression
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Buy Softcover. FAQ Policy. About this book The regression estimation problem has a long history. Show all.
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Prewitt, Journal of the American Statistical Association, "This book presents a modern approach to nonparametric regression estimation with random design. Coolen, Kwantitatieve Methoden, Issue 70B38, "The monograph under review can be considered as the next volume in a series of seminal monographs on the theoretical foundations of nonparametric estimation …. Pages How to Construct Nonparametric Regression Estimates?
Lower Bounds Pages Partitioning Estimates Pages Kernel Estimates Pages Splitting the Sample Pages Cross-Validation Pages