The Annals of Statistics

Monotone Regression and Covariance Structure

Gerald Shea

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Abstract

The monotone regression of a variable $X$ on another variable $Y$ is of particular interest when $Y$ cannot be directly observed. The correlation of $X$ and $Y$ can be tested if at least high and low values of $Y$ can be recognized. If all the components of a random vector have monotone regression on a variable $Y$, and if they are all uncorrelated given $Y$, then an inequality due to Chebyshev shows that marginal zero covariances imply that all but at most one of the components are uncorrelated with $Y$. Cases are examined where marginal uncorrelatedness of attributes implies their independence. Applications to contaminated experiments and to discriminant analysis are noted.

Article information

Source
Ann. Statist. Volume 7, Number 5 (1979), 1121-1126.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344794

Digital Object Identifier
doi:10.1214/aos/1176344794

Mathematical Reviews number (MathSciNet)
MR536513

Zentralblatt MATH identifier
0423.62039

JSTOR
links.jstor.org

Subjects
Primary: 62H20: Measures of association (correlation, canonical correlation, etc.)
Secondary: 62H05: Characterization and structure theory

Keywords
Covariance independence monotone regression quadrant dependence statistical diagnosis

Citation

Shea, Gerald. Monotone Regression and Covariance Structure. Ann. Statist. 7 (1979), no. 5, 1121--1126. doi:10.1214/aos/1176344794. https://projecteuclid.org/euclid.aos/1176344794.


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