Nonparametric regression with filtered data
(2011) Bernoulli : a journal of mathematical statistics and probability — Vol. 17, n° 1, p. 60-87 (2011)
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- Authors
- Abstract
- We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
- Affiliations
London School of Economics Department of Statistics University of Mannheim Department of Economics
Citations
Linton, O., Mammen, E., Nielsen, J. P., & Van Keilegom, I. (2011). Nonparametric regression with filtered data. Bernoulli : a journal of mathematical statistics and probability, 17(1), 60-87. https://doi.org/10.3150/10-BEJ260 (Original work published 2011)