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Faculty of Economics

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Linton, O., Mammen, E., Nielsen, J. P. and Van Keilegom, I.

Nonparametric Regression with Filtered Data

Bernoulli

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.

Author links: Oliver Linton  

Publisher's Link: http://projecteuclid.org/euclid.bj/1297173833



Papers and Publications



Recent Publications


Gagnon, J. and Goyal, S. Networks, markets and inequality American Economic Review [2017]

Elliott, M. and Golub, B. A network approach to public goods accepted, Journal of Political Economy [2018]

Onatski, A. and Wang, C. Alternative Asymptotics for Cointegration Tests in Large VARs Econometrica [2018]