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SimReg: a software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models

SimReg: a software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models
SimReg: a software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models
The problem of simultaneous inference and multiple comparison for comparing means of k(?3) populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known.
Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test.
linear regression, multiple comparison, simultaneous confidence bands, partial F test, statistic software.
1-22
Jamishidian, Mortaza
58e22cc5-17da-4051-9ef9-8603ffcb4685
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Zhang, Ying
a1a5b530-992a-41b3-94d8-043590122036
Jamshidian, Farid
7c2f5c7e-f834-4c52-9631-ad30e35c8d5e
Jamishidian, Mortaza
58e22cc5-17da-4051-9ef9-8603ffcb4685
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Zhang, Ying
a1a5b530-992a-41b3-94d8-043590122036
Jamshidian, Farid
7c2f5c7e-f834-4c52-9631-ad30e35c8d5e

Jamishidian, Mortaza, Liu, Wei, Zhang, Ying and Jamshidian, Farid (2005) SimReg: a software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models. Journal of Statistical Software, 12 (2), 1-22.

Record type: Article

Abstract

The problem of simultaneous inference and multiple comparison for comparing means of k(?3) populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known.
Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test.

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More information

Published date: 2005
Keywords: linear regression, multiple comparison, simultaneous confidence bands, partial F test, statistic software.
Organisations: Statistics

Identifiers

Local EPrints ID: 30125
URI: http://eprints.soton.ac.uk/id/eprint/30125
PURE UUID: 3ce7f056-7bc8-4bd6-8c48-afa430cc16f0
ORCID for Wei Liu: ORCID iD orcid.org/0000-0002-4719-0345

Catalogue record

Date deposited: 11 May 2006
Last modified: 28 Apr 2022 01:36

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Contributors

Author: Mortaza Jamishidian
Author: Wei Liu ORCID iD
Author: Ying Zhang
Author: Farid Jamshidian

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