Nonparametric Estimation of the Conditional Distribution at Regression Boundary Points

Srinjoy Das and Dimitris N. Politis

The American Statistician, 2020, vol. 74, issue 3, 233-242

Abstract: Nonparametric regression is a standard statistical tool with increased importance in the Big Data era. Boundary points pose additional difficulties but local polynomial regression can be used to alleviate them. Local linear regression, for example, is easy to implement and performs quite well both at interior and boundary points. Estimating the conditional distribution function and/or the quantile function at a given regressor point is immediate via standard kernel methods but problems ensue if local linear methods are to be used. In particular, the distribution function estimator is not guaranteed to be monotone increasing, and the quantile curves can “cross.” In the article at hand, a simple method of correcting the local linear distribution estimator for monotonicity is proposed, and its good performance is demonstrated via simulations and real data examples. Supplementary materials for this article are available online.

Date: 2020
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DOI: 10.1080/00031305.2018.1558109

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