On nonparametric estimation of mean functionals
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.
Year of publication: |
1998
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Authors: | Galindo, Christian D. |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 39.1998, 2, p. 143-149
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Publisher: |
Elsevier |
Keywords: | Asymptotic theory Kernel estimation Mean functional estimation Missing Data Nonparametric Regression |
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