The Annals of Mathematical Statistics

Monotone Median Regression

J. D. Cryer, Tim Robertson, F. T. Wright, and Robert J. Casady

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Abstract

Suppose that for each real number $t$ in [0, 1] we have a distribution with distribution function $F_t(\bullet)$, mean $\mu(t)$ and median $m(t) (\mu(t)$ and $m(t)$ are referred to as regression functions). Consider the problems of estimating $\mu(\bullet)$ and $m(\bullet)$. In this paper we propose and discuss an estimator, $\hat{m}(\bullet)$, of $m(\bullet)$ which is monotone. This estimator is analogous to the estimator $\hat{\mu}(\bullet)$ of $\mu(\bullet)$ which was explored by Brunk (1970) (Estimation of isotonic regression in Nonparametric Techniques in Statistical Inference, Cambridge University Press, 177-195). Rates for the convergence of $\hat{m}(\bullet)$ to $m(\bullet)$ are given and a simulation study, where $\hat{m}(\bullet), \hat{\mu}(\bullet)$ and the least squares linear estimator are compared, is discussed.

Article information

Source
Ann. Math. Statist., Volume 43, Number 5 (1972), 1459-1469.

Dates
First available in Project Euclid: 27 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aoms/1177692378

Digital Object Identifier
doi:10.1214/aoms/1177692378

Mathematical Reviews number (MathSciNet)
MR370903

Zentralblatt MATH identifier
0251.62025

JSTOR
links.jstor.org

Citation

Cryer, J. D.; Robertson, Tim; Wright, F. T.; Casady, Robert J. Monotone Median Regression. Ann. Math. Statist. 43 (1972), no. 5, 1459--1469. doi:10.1214/aoms/1177692378. https://projecteuclid.org/euclid.aoms/1177692378


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