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.
Publié le : 2011-02-15
Classification:
censoring,
counting process theory,
hazard functions,
kernel estimation,
local linear estimation,
truncation
@article{1297173833,
author = {Linton, Oliver and Mammen, Enno and Nielsen, Jens Perch and Van Keilegom, Ingrid},
title = {Nonparametric regression with filtered data},
journal = {Bernoulli},
volume = {17},
number = {1},
year = {2011},
pages = { 60-87},
language = {en},
url = {http://dml.mathdoc.fr/item/1297173833}
}
Linton, Oliver; Mammen, Enno; Nielsen, Jens Perch; Van Keilegom, Ingrid. Nonparametric regression with filtered data. Bernoulli, Tome 17 (2011) no. 1, pp. 60-87. http://gdmltest.u-ga.fr/item/1297173833/