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June, 1983 The Min-Max Algorithm and Isotonic Regression
Chu-In Charles Lee
Ann. Statist. 11(2): 467-477 (June, 1983). DOI: 10.1214/aos/1176346153

Abstract

There is growing interest in statistical inference under order restrictions. A major demand in this subject is to have a fast, direct method to solve the least squares problem of partially ordered isotonic regression. The Min-Max algorithm is such a method in which the user searches for the global minimum and the local maximum successively. A comparison of algorithms for partially ordered isotonic regression is included. As an application, using this efficient algorithm, it is feasible to approximate critical values of isotonic tests by simulation.

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Chu-In Charles Lee. "The Min-Max Algorithm and Isotonic Regression." Ann. Statist. 11 (2) 467 - 477, June, 1983. https://doi.org/10.1214/aos/1176346153

Information

Published: June, 1983
First available in Project Euclid: 12 April 2007

zbMATH: 0521.62060
MathSciNet: MR696059
Digital Object Identifier: 10.1214/aos/1176346153

Subjects:
Primary: 62F10
Secondary: 62F03

Rights: Copyright © 1983 Institute of Mathematical Statistics

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Vol.11 • No. 2 • June, 1983
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