Multifactor B-spline mixed models in designed experiments for the engine mapping problem
Multifactor B-spline mixed models in designed experiments for the engine mapping problem
Polynomial spline regression models using B-spline basis functions are shown to provide greater flexibility and lead to more accurate predictions than traditional polynomial models. Use of multifactor B-spline regression models is described in a case study from the automotive industry in engine mapping using a two-stage hierarchical model. The accuracy and effectiveness of the approach is demonstrated with validation runs.
380-391
Grove, D.M.
f99f9b0b-df7e-450f-a796-8b5a7a811d43
Woods, D.C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
2004
Grove, D.M.
f99f9b0b-df7e-450f-a796-8b5a7a811d43
Woods, D.C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
Grove, D.M., Woods, D.C. and Lewis, S.M.
(2004)
Multifactor B-spline mixed models in designed experiments for the engine mapping problem.
Journal of Quality Technology, 36 (4), .
Abstract
Polynomial spline regression models using B-spline basis functions are shown to provide greater flexibility and lead to more accurate predictions than traditional polynomial models. Use of multifactor B-spline regression models is described in a case study from the automotive industry in engine mapping using a two-stage hierarchical model. The accuracy and effectiveness of the approach is demonstrated with validation runs.
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Published date: 2004
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Statistics
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Local EPrints ID: 29948
URI: http://eprints.soton.ac.uk/id/eprint/29948
PURE UUID: 80ce23a7-1f90-4517-be82-134808c8c576
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Date deposited: 11 May 2006
Last modified: 09 Jan 2022 03:03
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Author:
D.M. Grove
Author:
S.M. Lewis
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