Bernoulli

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  • Volume 12, Number 3 (2006), 469-490.

A simple nonparametric estimator of a strictly monotone regression function

Holger Dette, Natalie Neumeyer, and Kay F. Pilz

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Abstract

A new method for monotone estimation of a regression function is proposed, which is potentially attractive to users of conventional smoothing methods. The main idea of the new approach is to construct a density estimate from the estimated values m̂ (i/N) ( i =1,…,N ) of the regression function and to use these `data' for the calculation of an estimate of the inverse of the regression function. The final estimate is then obtained by a numerical inversion. Compared to the currently available techniques for monotone estimation the new method does not require constrained optimization. We prove asymptotic normality of the new estimate and compare the asymptotic properties with the unconstrained estimate. In particular, it is shown that for kernel estimates or local polynomials the bandwidths in the procedure can be chosen such that the monotone estimate is first-order asymptotically equivalent to the unconstrained estimate. We also illustrate the performance of the new procedure by means of a simulation study.

Article information

Source
Bernoulli, Volume 12, Number 3 (2006), 469-490.

Dates
First available in Project Euclid: 28 June 2006

Permanent link to this document
https://projecteuclid.org/euclid.bj/1151525131

Digital Object Identifier
doi:10.3150/bj/1151525131

Mathematical Reviews number (MathSciNet)
MR2232727

Zentralblatt MATH identifier
1100.62045

Keywords
isotone regression local linear regression Nadaraya-Watson estimator order-restricted inference

Citation

Dette, Holger; Neumeyer, Natalie; Pilz, Kay F. A simple nonparametric estimator of a strictly monotone regression function. Bernoulli 12 (2006), no. 3, 469--490. doi:10.3150/bj/1151525131. https://projecteuclid.org/euclid.bj/1151525131


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