Metodos Nao Parametricos
 

SINAPE 2004 Tutorial em R

Bibliografia: Introducao

• Conover, W.J. Practical Nonparametric statistics. John Wiley, 2ed.

• Silverman B. W. Density Estimation: for statistics and data analysis. Chapman & Hall.

• Hardle, W. Smoothing Techniques with implementation in S. Springer Verlag.

• Pagan, A. and Ullah, A. Nonparametric Econometrics. Cambridge Press.

• Eubank, R. L. Spline smoothing and nonparametric regression. Marcel Dekker, INC.

Softwares: R, S-plus . A linguagem R pode ser obtida gratuitamente de  www.R-project.org

Leituras  recomendadas:

• Dias, R. (2001). A review of non-parametric curve estimation methods with applications to Econometrics.   Economia, Vol. 2 (2) 2001. To appear.    RP 05/01,  IMECC UNICAMP   (Technical Report)   PDF file

• Dias, R. (2001). O uso de splines em regressao nao parametrica.  PDF file

• Dias, R. (2001). Regressao Nao Parametrica. PDF file

• Wahba, G. (2000). Splines in Nonparametric Regression. Encyclopedia of Environmetrics.  PDF file

• Morettin P. A. (1999). Ondas e Ondaletas: Da analise de Fourier a analise de Ondaletas (Capitulos 6 e 7). Editora da USP-SP.

• Dias, R. (1999). A note on Maximum likelihood density estimation using a proxy of the Kullback-Leibler distance. Braz. Jrnl. of Prob. & Stat., vol. 13. no. 2 pp. 181.

• Dias,  R. (1999). Sequential Adaptive Nonparametric Regression via  H-splines.Communications in Statistics: Computation and Simulation, Volume 28,  pp  501-515, 1999.Postscript(Technical Report)  RP 43/96, IMECC, UNICAMP.

• Dias,  R. (1998). Density Estimation via Hybrid splines.  Jornl. of Statl. Comp. & Simul. vol. 60 pp.277-293.

• Wahba, G. (1990). Spline Models for observational Data (Chapter I). Siam:PA.

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