Nonparametric kernel regression subject to monotonicity constraints
نویسندگان
چکیده
منابع مشابه
Nonparametric Kernel Regressionsubject to Monotonicity
We suggest a biased-bootstrap method for monotonising general linear, kernel-type estimators, for example local linear estimators and Nadaraya-Watson estimators. Attributes of our approach include the fact that it produces smooth estimates, that is applicable to a particularly wide range of estimator types, and that it can be employed after the smoothing step has been implemented. Therefore , a...
متن کاملNONPARAMETRIC KERNEL REGRESSION SUBJECT TO MONOTONICITY CONSTRAINTS By Peter Hall and Li-Shan Huang Australian National University and CSIRO and Australian National University
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 es...
متن کاملNonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints
Nonparametric smoothing under shape constraints has recently received much well-deserved attention. Powerful methods have been proposed for imposing a single shape constraint such as monotonicity and concavity on univariate functions. In this paper, we extend the monotone kernel regression method in Hall and Huang (2001) to the multivariate and multi-constraint setting. We impose equality and/o...
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This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to show their finite sample performance. An application to predicting equity premium is taken for illustr...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2001
ISSN: 0090-5364
DOI: 10.1214/aos/1009210683