The Annals of Statistics

The Min-Max Algorithm and Isotonic Regression

Chu-In Charles Lee

Full-text: Open access

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.

Article information

Source
Ann. Statist., Volume 11, Number 2 (1983), 467-477.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346153

Digital Object Identifier
doi:10.1214/aos/1176346153

Mathematical Reviews number (MathSciNet)
MR696059

Zentralblatt MATH identifier
0521.62060

JSTOR
links.jstor.org

Subjects
Primary: 62F10: Point estimation
Secondary: 62F03: Hypothesis testing

Keywords
Min-Max algorithm isotonic regression likelihood ratio tests

Citation

Lee, Chu-In Charles. The Min-Max Algorithm and Isotonic Regression. Ann. Statist. 11 (1983), no. 2, 467--477. doi:10.1214/aos/1176346153. https://projecteuclid.org/euclid.aos/1176346153


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