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Fractional polynomial models for constrained mixture experiments

Fractional polynomial models for constrained mixture experiments
Fractional polynomial models for constrained mixture experiments
A new - parsimonious but flexible - class of non-linear models, based on Fractional Polynomials, is proposed for fitting the data from constrained mixture experiments. These Mixture Fractional Polynomial (MFP) Models are easily fitted by nonlinear least squares using a partially linear algorithm. They are compared with the recently proposed class of nonlinear General Blending Models, and with several commonly used linear models from the literature. It is shown that the new class of MFP Models outperforms those competing models in several practical applications.
Nonlinear Regression, Mixture Components, Compositional Data, Linear Constraints
Khashab, Rana
6b787441-a41f-4fa0-baf4-45cc6aa5990e
Gilmour, Steven G
984dbefa-893b-444d-9aa2-5953cd1c8b03
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Khashab, Rana
6b787441-a41f-4fa0-baf4-45cc6aa5990e
Gilmour, Steven G
984dbefa-893b-444d-9aa2-5953cd1c8b03
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039

Khashab, Rana, Gilmour, Steven G and Biedermann, Stefanie (2019) Fractional polynomial models for constrained mixture experiments. Pre-print. (Submitted)

Record type: Article

Abstract

A new - parsimonious but flexible - class of non-linear models, based on Fractional Polynomials, is proposed for fitting the data from constrained mixture experiments. These Mixture Fractional Polynomial (MFP) Models are easily fitted by nonlinear least squares using a partially linear algorithm. They are compared with the recently proposed class of nonlinear General Blending Models, and with several commonly used linear models from the literature. It is shown that the new class of MFP Models outperforms those competing models in several practical applications.

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More information

In preparation date: 2019
Submitted date: 19 March 2019
Keywords: Nonlinear Regression, Mixture Components, Compositional Data, Linear Constraints

Identifiers

Local EPrints ID: 429243
URI: https://eprints.soton.ac.uk/id/eprint/429243
PURE UUID: 8f57ada9-a2fa-4f23-9c42-af449f688f5a

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Date deposited: 25 Mar 2019 17:30
Last modified: 25 Mar 2019 17:30

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