Model selection in linear regression using the MML criterion

Rohan A. Baxter; David L. Dowe

Model selection in linear regression using the MML criterion

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Abstract

We use the minimum message length (MML) criterion for model selection in regression problems. The MML criterion uses an accurate estimate of the length of a code for the parameters of a Bayesian density. We have applied the MML criterion to the classic problem of choosing the degree of a polynomial in lease-squares regression. The MML criterion performed well in Monte Carlo experiments with small and/or noisy data sets relative to the other criteria examined.

Original language	English
Title of host publication	Proceedings of the Data Compression Conference
Editors	James A. Storer, Martin Cohn
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	1
ISBN (Print)	0818656379
Publication status	Published - 1994
Event	Data Compression Conference 1994 - Snowbird, United States of America Duration: 29 Mar 1994 → 31 Mar 1994 Conference number: 4th https://ieeexplore.ieee.org/xpl/conhome/961/proceeding (Proceedings)

Conference

Conference	Data Compression Conference 1994
Abbreviated title	DCC 1994
Country/Territory	United States of America
City	Snowbird
Period	29/03/94 → 31/03/94
Internet address	https://ieeexplore.ieee.org/xpl/conhome/961/proceeding (Proceedings)

Cite this

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title = "Model selection in linear regression using the MML criterion",

abstract = "We use the minimum message length (MML) criterion for model selection in regression problems. The MML criterion uses an accurate estimate of the length of a code for the parameters of a Bayesian density. We have applied the MML criterion to the classic problem of choosing the degree of a polynomial in lease-squares regression. The MML criterion performed well in Monte Carlo experiments with small and/or noisy data sets relative to the other criteria examined.",

author = "Baxter, {Rohan A.} and Dowe, {David L.}",

year = "1994",

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note = "Data Compression Conference 1994, DCC 1994 ; Conference date: 29-03-1994 Through 31-03-1994",

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AB - We use the minimum message length (MML) criterion for model selection in regression problems. The MML criterion uses an accurate estimate of the length of a code for the parameters of a Bayesian density. We have applied the MML criterion to the classic problem of choosing the degree of a polynomial in lease-squares regression. The MML criterion performed well in Monte Carlo experiments with small and/or noisy data sets relative to the other criteria examined.

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M3 - Conference Paper

AN - SCOPUS:0027969413

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A2 - Cohn, Martin

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - Data Compression Conference 1994

Y2 - 29 March 1994 through 31 March 1994

ER -

Model selection in linear regression using the MML criterion

Abstract

Conference

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