Estimation of integrated squared density derivatives from a contaminated sample
AbstractWe propose a kernel estimator of integrated squared density derivatives, from a sample that has been contaminated by random noise. We derive asymptotic expressions for the bias and the variance of the estimator and show that the squared bias term dominates the variance term. This coincides with results that are available for non-contaminated observations. We then discuss the selection of the bandwidth parameter when estimating integrated squared density derivatives based on contaminated data. We propose a data-driven bandwidth selection procedure of the plug-in type and investigate its finite sample performance via a simulation study. Copyright 2002 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal Of The Royal Statistical Society Series B.
Volume (Year): 64 (2002)
Issue (Month): 4 ()
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- Mia Hubert & Irène Gijbels & Dina Vanpaemel, 2013. "Reducing the mean squared error of quantile-based estimators by smoothing," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, Springer, vol. 22(3), pages 448-465, September.
- Birke, Melanie & Bissantz, Nicolai & Holzmann, Hajo, 2008. "Confidence bands for inverse regression models with application to gel electrophoresis," Technical Reports 2008,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Delaigle, A. & Gijbels, I., 2004. "Practical bandwidth selection in deconvolution kernel density estimation," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 45(2), pages 249-267, March.
- Stéphane Bonhomme & Jean-Marc Robin, 2008.
"Generalized nonparametric deconvolution with an application to earnings dynamics,"
CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies
CWP03/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Stéphane Bonhomme & Jean-Marc Robin, 2010. "Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 491-533.
- William C. Horrace & Christopher F. Parmeter, 2008.
"Semiparametric Deconvolution with Unknown Error Variance,"
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104, Center for Policy Research, Maxwell School, Syracuse University.
- William Horrace & Christopher Parmeter, 2011. "Semiparametric deconvolution with unknown error variance," Journal of Productivity Analysis, Springer, Springer, vol. 35(2), pages 129-141, April.
- Holzmann, Hajo & Bissantz, Nicolai & Munk, Axel, 2007. "Density testing in a contaminated sample," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 98(1), pages 57-75, January.
- Bissantz, Nicolai & Birke, Melanie, 2008. "Asymptotic normality and confidence intervals for inverse regression models with convolution-type operators," Technical Reports 2008,17, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Staudenmayer, John & Ruppert, David & Buonaccorsi, John P., 2008. "Density Estimation in the Presence of Heteroscedastic Measurement Error," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 103, pages 726-736, June.
- Bissantz, Nicolai & Birke, Melanie, 2009. "Asymptotic normality and confidence intervals for inverse regression models with convolution-type operators," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 100(10), pages 2364-2375, November.
- Delaigle, A. & Gijbels, I., 2006. "Data-driven boundary estimation in deconvolution problems," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 50(8), pages 1965-1994, April.
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