The Annals of Statistics

On Model Selection and the ARC Sine Laws

Michael Woodroofe

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Abstract

Generalizations of the arc sine laws are shown to provide insight into the operating characteristics of certain techniques for selecting models to fit a given data set, when the available models are nested. As a corollary, one sees that a popular technique may be expected to include about one superfluous parameter, even if the sample size is large.

Article information

Source
Ann. Statist., Volume 10, Number 4 (1982), 1182-1194.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345983

Digital Object Identifier
doi:10.1214/aos/1176345983

Mathematical Reviews number (MathSciNet)
MR673653

Zentralblatt MATH identifier
0507.62037

JSTOR
links.jstor.org

Subjects
Primary: 62F99: None of the above, but in this section
Secondary: 62J05: Linear regression

Keywords
Akaike's criterion Asymptotic distributions Mallows $C_p$ Random walks

Citation

Woodroofe, Michael. On Model Selection and the ARC Sine Laws. Ann. Statist. 10 (1982), no. 4, 1182--1194. doi:10.1214/aos/1176345983. https://projecteuclid.org/euclid.aos/1176345983


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