Nonparametric kernel regression subject to monotonicity constraints
We suggest a method for monotonizing general kernel-type estimators, for example local linear estimators and Nadaraya .Watson estimators. Attributes of our approach include the fact that it produces smooth estimates, indeed with the same smoothness as the unconstrained estimate. The method is applicable to a particularly wide range of estimator types, it can be trivially modified to render an estimator strictly monotone and it can be employed after the smoothing step has been implemented....[Show more]
|Collections||ANU Research Publications|
|Source:||The Annals of Statistics|
|Access Rights:||Open Access|
|01_Hall_Nonparametric_Kernel_2001.pdf||250.5 kB||Adobe PDF|
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