 Finite sample performance of deconvolving density estimatorsM. P. Wand Statistics & Probability Letters, 1998, vol. 37, issue 2, 131-139 Abstract: Recent studies have shown that the asymptotic performance of nonparametric curve estimators in the presence of measurement error will often be very much inferior to that when the observations are error-free. For example, deconvolution of Gaussian measurement error worsens the usual algebraic convergence rates of kernel estimators to very slow logarithmic rates. However, the slow convergence rates mean that very large sample sizes may be required for the asymptotics to take effect, so the finite sample properties of the estimator may not be very well described by the asymptotics. In this article finite sample calculations are performed for the important cases of Gaussian and Laplacian measurement error which provide insight into the feasibility of deconvolving density estimators for practical sample sizes. Our results indicate that for lower levels of measurement error deconvolving density estimators can perform well for reasonable sample sizes. Keywords: Errors-in-variables; Kernel; estimator; Measurement; error; Mean; integrated; squared; error; Nonparametric; regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:37:y:1998:i:2:p:131-139 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.