Smoothing: Local regression techniques

Handbook of Computational Statistics: Concepts and Methods: Second Edition

Volumn , Issue , 2012, Pages 571-596

  Loader, Catherine ^a  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LOCAL REGRESSION;

EID: 84963672134     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-21551-3__20     Document Type: Chapter

References (34)

1. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csàki, F. (eds.) Second International Symposium on Information Theory, pp. 267-281. Budapest, Akademia Kiadó (1972)
2. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716-723 (1974)
3. Azzalini, A., Bowman, A.W.:Applied Smoothing Techniques for Data Analysis. Oxford University Press, Oxford (1997)
4. Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829-836 (1979)
5. Cleveland, W.S., Devlin, S.J.: Locally weighted regression: An approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596-610 (1988)
6. Efromovich, S.: Nonparametric Curve Estimation. Springer, New York (1999)
7. Fan, J., Gijbels, I.: Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation. J. Roy. Stat. Soc. B 57, 371-394 (1995)
8. Fan, J., Gijbels, I.: Local Polynomial Modelling and its Applications. Chapman and Hall, London (1996)
9. Friedman, J., Stuetzle, W.: Projection pursuit regression. J. Am. Stat. Assoc. 76, 817-823 (1981)
10. Green, P.J., Silverman, B.: Nonparametric regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman and Hall, London (1994)
11. Härdle, W.: Applied Nonparametric Regression. Cambridge University Press, Cambridge (1990)
12. Hart, J.D.: Nonparametric Smoothing and Lack-of-Fit Tests. Springer, New York (1997)
13. Hastie, T.J., Loader, C.R.: Local regression: Automatic kernel carpentry (with discussion). Stat. Sci. 8, 120-143 (1993)
14. Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990)
15. Henderson, R.: Note on graduation by adjusted average. Trans. Actuarial Soc. Am. 17, 43-48 (1916)
16. Henderson, R.: A new method of graduation. Trans. Actuarial Soc. Am. 25, 29-40 (1924)
17. Hjort, N.L., Jones, M.C.: Locally parametric nonparametric density estimation. Ann. Stat. 24, 1619-1647 (1996)
18. Katkovnik, V.Y.: Linear and nonlinear methods of nonparametric regression analysis. Soviet Autom. Contr. 5, 35-46 (25-34) (1979)
19. Loader, C.: Local likelihood density estimation. Ann. Stat. 24, 1602-1618 (1996)
20. Loader, C.: Bandwidth selection: Classical or plug-in? Ann. Stat. 27, 415-438 (1999a)
21. Loader, C.: Local Regression and Likelihood. Springer, New York (1999b)
22. p. Technometrics 15, 661-675 (1973)
23. Nadaraya, E.A.: On estimating regression. Theor. Probab. Appl. 9, 157-159 (141-142) (1964)
24. Ruppert, D., Sheather, S.J., and Wand,M.P.: An effective bandwidth selector for local least squares regression. J. Am. Stat. Assoc. 90, 1257-1270 (1995)
25. Ruppert, D., Wand, M.P.: Multivariate locally weighted least squares regression. Ann. Stat. 22, 1346-1370 (1994)
26. Ruppert, D., Wand, M.P., Carroll, R.J.: Semiparametric Regression. Cambridge University Press, Cambridge (2003)
27. Schiaparelli, G.V.: Sul modo di ricavare la vera espressione delle leggi delta natura dalle curve empiricae. Effemeridi Astronomiche di Milano per l'Arno 857, 3-56 (1866)
28. Silverman, B.W.: Some aspects of the spline smoothing approach to nonparametric regression curve fitting (with discussion). J. Roy. Stat. Soc. B 47, 1-52 (1985)
29. Stone, C.J.: Consistent nonparametric regression (with discussion). Ann. Stat. 5, 595-645 (1977)
30. Tibshirani, R.J., Hastie, T.J.: Local likelihood estimation. J. Am. Stat. Assoc. 82, 559-567 (1987)
31. Wahba, G.: Spline Models for Observational Data, SIAM, Philadelphia (1990)
32. Wahba, G., Wold, S.: A completely automatic French curve: Fitting spline functions by crossvalidation. Comm. Stat. 4, 1-17 (1975)
33. Watson, G.S.: Smooth regression analysis. Sankhya A 26, 359-372 (1964)
34. Whitaker, E.T.: On a new method of graduation. Proc. Edinb. Math. Soc. 41, 62-75 (1923)