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Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems

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Author Info
Hall, Peter
Abstract

We describe a bootstrap method for estimating mean squared error and smoothing parameter in nonparametric problems. The method involves using a resample of smaller size than the original sample. There are many applications, which are illustrated using the special cases of nonparametric density estimation, nonparametric regression, and tail parameter estimation.

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Publisher Info
Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 32 (1990)
Issue (Month): 2 (February)
Pages: 177-203
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Handle: RePEc:eee:jmvana:v:32:y:1990:i:2:p:177-203

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Keywords: bias bootstrap density estimation mean squared error nonparametric regression smoothing parameter;

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  1. Luis Fernando Melo Velandia & Oscar Reinaldo Becerra Camargo, 2005. "Medidas De Riesgo, Caracteristicas Y Técnicas De Medición: Una Aplicación Del Var Y El Es A La Tasa Interbancaria De Colombia," BORRADORES DE ECONOMIA 003198, BANCO DE LA REPÚBLICA. [Downloadable!]
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  2. Peter Hall & Raoul LePage, 1996. "On bootstrap estimation of the distribution of the studentized mean," Annals of the Institute of Statistical Mathematics, Springer, vol. 48(3), pages 403-421, September. [Downloadable!] (restricted)
  3. Jondeau, E. & Rockinger, M., 1999. "The Tail Behavior of Sotck Returns: Emerging Versus Mature Markets," Documents de Travail 66, Banque de France. [Downloadable!]
  4. Juan M. Vilar Fernández & Alejandro Quintela del Río, 1993. "Técnicas no paramétricas de estimación funcional, con observaciones dependientes," Investigaciones Economicas, Fundación SEPI, vol. 17(1), pages 143-163, January. [Downloadable!]
  5. Patrice Bertail & Christian Haefke & D Politis & Halbert White, 2000. "A Subsampling Approach to Estimating The Distribution of Diverging Statistics with Applications to Assessing Financial Market Risk," University of California at San Diego, Economics Working Paper Series 2000-01, Department of Economics, UC San Diego. [Downloadable!]
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  6. Dennis W. Jansen, 2001. "Limited Downside Risk In Portfolio Selection Among U.S. And Pacific Basin Equities," International Economic Journal, Korean International Economic Association, vol. 15(4), pages 1-22, December. [Downloadable!] (restricted)
  7. Duc Devroye & J. Beirlant & R. Cao & R. Fraiman & P. Hall & M. Jones & Gábor Lugosi & E. Mammen & J. Marron & C. Sánchez-Sellero & J. Uña & F. Udina & L. Devroye, 1997. "Universal smoothing factor selection in density estimation: theory and practice," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 6(2), pages 223-320, December. [Downloadable!] (restricted)
  8. Chollete, Loran & de la Pena , Victor & Lu, Ching-Chih, 2009. "International Diversification: An Extreme Value Approach," UiS Working Papers in Economics and Finance 2009/26, University of Stavanger. [Downloadable!]
  9. A. Delaigle & I. Gijbels, 2004. "Bootstrap bandwidth selection in kernel density estimation from a contaminated sample," Annals of the Institute of Statistical Mathematics, Springer, vol. 56(1), pages 19-47, March. [Downloadable!] (restricted)
  10. J. Beirlant & A. Berlinet & G. Biau, 2008. "Higher order estimation at Lebesgue points," Annals of the Institute of Statistical Mathematics, Springer, vol. 60(3), pages 651-677, September. [Downloadable!] (restricted)
  11. J. Danielsson & L. de Haan & L. Peng & C.G. de Vries, 2000. "Using a bootstrap method to choose the sample fraction in tail index estimation," Econometric Institute Report 197, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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