Métodos de regresión no paramétrica

Arango, J. & Calderón, G. (2006),Notas de clase: EcuacionesDiferenciales, Departamento de Matemáticas, Universidad delValle, Cali, Colombia.

Barrientos, A. F., Olaya, J. & Gonzalez, V. M. (2007), 'Un modelospline para el pronóstico de la demanda de energía eléctrica',Revista Colombiana de Estadística30(2), 187-202.

Bartle, R. G. (1995),The Elements of Integration and LebesgueMeasure, Wiley, New York, NY. https://doi.org/10.1002/9781118164471

Benedetti, G. (1975), 'Kernel estimation of regresion functions,Proceedings in Computer Science and Statistics:8thannualsymposium on the interfacepp. 405-412.

Bowman, A. W. & Azzalini, A. (1997),Applied Smoothing Techniquesfor Data Analysis. The Kernel Approach with S-plus Illustrations,Oxford. https://doi.org/10.1093/oso/9780198523963.001.0001

Bowman, A. W. & Azzalini, A. (2010),R packagesm: nonparametricsmoothing methods (version 2.2-4), University of Glasgow, UKand Università di Padova, Italia.

Breiman, L. (1991), 'Theπmethod for estimating multivariatefunctions from noisy data',Technometrics33(2), 125-160.

https://doi.org/10.2307/1269038

Brown, L. D. & Levine, M. (2007), 'Variance estimation innonparametric regression via the difference sequence method',The Annals of Statistics35, 2219-2232. https://doi.org/10.1214/009053607000000145

Cleveland, W. S. (1979), 'Robust locally weighted regression andsmoothing scatterplots',Journal of the American StatisticalAssociation74, 829-836. https://doi.org/10.1080/01621459.1979.10481038

Delicado, P. (2008),Curso de Modelos no Paramétricos, UPC

Diggle, P. J., Heagerty, P., Liang, K.-Y. & Zeger, S. L. (2002),Analysisof Longitudinal Data, 2nd. edn, Oxford. https://doi.org/10.1093/oso/9780198524847.001.0001

Draper, N. R. & Smith, H. (1966),Applied Regression Analysis, JohnWiley & Sons, New York, NY.

Draper, N. R. & Smith, H. (1998),Applied Regression Analysis, 3ra.edn, John Wiley & Sons, New York, NY. https://doi.org/10.1002/9781118625590

Epanechnikov, V. (1969), 'Non-parametric estimation of a multivariateprobability density',Theory of Probability and its Applications14, 153-158. https://doi.org/10.1137/1114019

Eubank, R. L. (1999),Nonparametric Regression and SplineSmoothing, second edn, Marcel Dekker, New York, NY. https://doi.org/10.1201/9781482273144

Fan, J. & Gijbels, I. (1996),Local Polynomial Modelling and ItsApplications, Chapman & Hall/CRC.

Ferraty, F., Nuñez Antón, V. & Vieu, P. (2001),Regresión noParamétrica: Desde la Dimensión Uno Hasta la DimensiónInfinita, UPV/EHU.

Ferraty, F. & Vieu, P. (2006),Nonparametric Functional Data AnalysisTheory and Practice, Springer.

Friedman, J. H. & Stuetzle, W. (1981), 'Projection pursuit regression,Journal of the American Statistical Association, Theory andMethods76(376), 817-823. https://doi.org/10.1080/01621459.1981.10477729

Fromkorth, A. & Kohler, M. (2011), 'Estimating the mean andcovariance structure nonparametrically when the data are curves',Journal of the Royal Statistical Society, Series B141, 172-188.

Gasser, T. & Müller, H. G. (1979),Smoothing Techniques for CurveEstimation, Springer, Heidelberg. https://doi.org/10.1007/BFb0098486

Gasser, T., Sroka, L. & Jennen-Steinmetz, C. (1986), 'Residualvariance and residual pattern in nonlinear regression',Biometrika73(3), 625-633. https://doi.org/10.1093/biomet/73.3.625

Gradshtein, I. S. & Ryzhik, I. M. (1980),Tables of Integrals, Series,and Products, Academic Press.

Graybill, F. A. (1976),Theory and Application of the Linear Model,Wadsworth & Brooks, Pacific Grove, California.

Graybill, F. A. (2001),Matrices with Applications in Statistics, secondedn, Cengage Learning.

Green, P. J. & Silverman, B. W. (2000),Nonparametric Regressionand Generalized Linear Models. A Roughness Penalty Approach,Chapman & Hall/CRC, Boca Raton, FL.

Hall, P. (1989), 'On projection pursuit regression',The Annals ofStatistics17(2), 573-588. https://doi.org/10.1214/aos/1176347126

Hall, P., Kay, J. W. & Titterington, D. M. (1990), 'Asymptoticallyoptimal difference-based estimation of variance in nonparametricregression',Biometrika 77(3), 521-528. https://doi.org/10.1093/biomet/77.3.521

Härdle, W. (1990a),Applied Nonparametric Regression, CambridgeUniversity Press, Cambridge, UK. https://doi.org/10.1017/CCOL0521382483

Härdle, W. (1990b),Smoothing Techniques, with Applications in S,Cambridge University Press, Cambridge, UK.

Härdle, W., Hall, P. & Ichimura, H. (1993), 'Optimal smoothing insingle-index models',The Annals of Statistics21(1), 157-178. https://doi.org/10.1214/aos/1176349020

Hastie, T. J. & Tibshirani, R. J. (1990),Generalized Additive Models,Chapman & Hall-CRC, Boca Raton, FL.

Junger, W. & de Leon, A. P. (2011),ares: Environment air pollutionepidemiology: a library for time series analysis. R package version0.7.2.URL:http://CRAN.R-project.org/package=ares

Kohler, M. & Krzyúzak, A. (2010), 'Pricing of american optionsin discrete time using least squares estimates with complexitypenalties'.

Kreyszig, E. (1989),Introductory Functional Analysis withApplications, Wiley, New York, NY.

Levine, M. (2006), 'Bandwidth selection for a class of difference-basedvariance estimators in the nonparametric regression: A possibleapproach',Comput. Stat. Data Anal.50, 3405-3431 https://doi.org/10.1016/j.csda.2005.08.001

Li, K.-C. (1991), 'Sliced inverse regression for dimension reduction',Journal of the American Statistical Association, Theory andMethods86(414), 316-327. https://doi.org/10.1080/01621459.1991.10475035

Müller, H. G. (1988),Nonparametric Regression Analysis ofLongitudinal Data, Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3926-0

Nadaraya, E. A. A. & Seckler, B. T. (1964), 'On estimating regression',Theory of Probability and its Applications (Transl of TeorijaVerojatnostei i ee Primenenija)9, 141-142. https://doi.org/10.1137/1109020

Nelder, J. A. & McCoullagh, P. (1989),Generalized linear models, 2edn, Chapman & Hall/CRC. https://doi.org/10.1007/978-1-4899-3242-6

Nelder, J. A. & Wedderburn, R. W. (1972), 'Generalized linearmodels',Journal of the Royal Statistical Society Series A135(3), 370-384.

https://doi.org/10.2307/2344614

Neter, J., Wasserman, W. & Kutner, M. H. (1990),Applied LinearStatistical Models, 3ra. edn, Irwin, Burr Ridge, Illinois.

Pereira, L. A., Paz, M. C. & Olaya, J. (2007), Estimación dela varianza en regresión no-paramétrica: El efecto de poseermúltiples observaciones por punto de diseño,in'17mo. Simposiode Estadística', Universidad Nacional de Colombia.

Priestley, M. & Chao, M. (1972), 'Non-parametric function fitting',Journal of the Royal Statistical Society, Series B34, 385-392. https://doi.org/10.1111/j.2517-6161.1972.tb00916.x

R Development Core Team (2011),R: A Language and Environmentfor Statistical Computing, R Foundation for StatisticalComputing, Vienna, Austria. ISBN 3-900051-07-0.URL:http://www.R-project.org/

Ramsay, J. O., Graves, S. & Hooker, G. (2010),Functional DataAnalysis withRandMATLAB, Springer.Ramsay, J. O. & Silverman, B. W. (2005),Functional Data Analysis,2nd. edn, Springer.Rice, J. A. (1984), 'Bandwidth choice for nonparametric regression',The Annals of Statistics12(4), 1215-1230.

Rice, J. A. & Silverman, B. W. (1991), 'Estimating the mean andcovariance structure nonparametrically when the data are curves',Journal of the Royal Statistical Society, Series B53, 233-243. https://doi.org/10.1111/j.2517-6161.1991.tb01821.x

Searle, S. R. (1971),Linear Models, John Wiley & Sons, New York,NY.

Seber, G. A. F. (1977),Linear Regression Analysis, John Wiley &Sons, New York, NY.

Sezer, A. (2009), 'Assesing the quality of the natural cubic splineapproximation',Proceedings of the 8th WSEAS InternationalConference on SYSTEM SCIENCE and SIMULATION inENGINEERINGpp. 186-190.

Simonoff, J. F. (1996),Smoothing Methods in Statistics, Springer, NewYork, NY. https://doi.org/10.1007/978-1-4612-4026-6

Singh, S. (1997),El enigma de Fermat, Planeta.

Sockett, E. B., Daneman, D., Clarson, C. & Ehrich, R. M. (1987),'Factors affecting and patterns of residual insulin secretion daringthe first year of type i (insulin dependent) diabetes mellitus inchildren',Diabet.30, 453-459. https://doi.org/10.1007/BF00279611

Takezawa, K. (2006),Introduction to Nonparametric Regression,Wiley. https://doi.org/10.1002/0471771457

Tong, T. & Wang, Y. (2005), 'Estimating residual variancein nonparametric regression using least squares',Biometrika93, 821-830. https://doi.org/10.1093/biomet/92.4.821

Wahba, G. (1990),Spline Models for Observational data, CBMS-NSFSeries, SIAM. https://doi.org/10.1137/1.9781611970128

Wang, J.-L. (2003), 'Nonparametric regression analysis of longitudinaldata.URL:http://www.stat.ucdavis.edu/~wang/paper/EOB3.pdf

Watson, G. (1964), 'Smooth regression analysis',Sankhya, series A26, 359-372.