| Document Type
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BL
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| Record Number
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655853
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| Doc. No
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dltt
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| Title & Author
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A distribution-free theory of nonparametric regression /\ László Györfi [and others]
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| Series Statement
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Springer series in statistics
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| Page. NO
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xvi, 647 pages :: illustrations ;; 25 cm
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| ISBN
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0387954414
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: 9780387954417
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| Bibliographies/Indexes
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Includes bibliographical references (pages 612-638) and indexes
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| Contents
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1. Why Is Nonparametric Regression Important? -- 2. How to Construct Nonparametric Regression Estimates? -- 3. Lower Bounds -- 4. Partitioning Estimates -- 5. Kernel Estimates -- 6. k-NN Estimates -- 7. Splitting the Sample -- 8. Cross-Validation -- 9. Uniform Laws of Large Numbers -- 10. Least Squares Estimates I: Consistency -- 11. Least Squares Estimates II: Rate of Convergence -- 12. Least Squares Estimates III: Complexity Regularization -- 13. Consistency of Data-Dependent Partitioning Estimates -- 14. Univariate Least Squares Spline Estimates -- 15. Multivariate Least Squares Spline Estimates -- 16. Neural Networks Estimates -- 17. Radial Basis Function Networks -- 18. Orthogonal Series Estimates -- 19. Advanced Techniques from Empirical Process Theory -- 20. Penalized Least Squares Estimates I: Consistency -- 21. Penalized Least Squares Estimates II: Rate of Convergence -- 22. Dimension Reduction Techniques -- 23. Strong Consistency of Local Averaging Estimates -- 24. Semirecursive Estimates -- 25. Recursive Estimates -- 26. Censored Observations -- 27. Dependent Observations
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| Subject
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Regression analysis
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| Subject
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Nonparametric statistics
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| Subject
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Distribution (Probability theory)
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| Subject
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Inferencia Estatistica
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| Subject
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Inferência não paramétrica
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| Subject
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Non-parametrische statistiek
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| Subject
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Regressieanalyse
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| Subject
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Verdelingen (statistiek)
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| Dewey Classification
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519.5/36
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| LC Classification
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QA278.2.D57 2002
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| Added Entry
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Györfi, László
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