 On nonparametric estimation of mean functionalsChristian D. Galindo Statistics & Probability Letters, 1998, vol. 39, issue 2, 143-149 Abstract: Cheng (1990, 1994) considered a missing data problem in which data comes in two forms, one in which a covariate X is observed, and the other in which both X and a response Y are observed. If the missing data probabilities are independent of X then the distribution of X is the same in the two populations. The goal is to estimate the marginal distribution of Y, and more specifically its mean. Cheng based his estimates on the regression of Y on X in the first population, using parametric and nonparametric regression, and showed that the two methods were roughly comparable in asymptotic efficiency. Motivated by a currently ongoing study, we consider a different problem, namely one in which the two populations are physically distinct in such a way that the distribution of X differs between the populations. We show that the nonparametric modification of Cheng's method appropriate to this situation has zero asymptotic efficiency relative to the parametric approach in a wide class of problems. Keywords: Asymptotic; theory; Kernel; estimation; Mean; functional; estimation; Missing; Data; 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:39:y:1998:i:2:p:143-149 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.