2009-05-15, English, Article, Journal or magazine article edition: Parametric sensitivity: A case study comparison. Sulieman, H.; Kucuk, I.; Mclellan, P.J.

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Physical Description
  • Magazine/​Journal article
Published
  • Elsevier B.V., 2009 May 15
Language
  • English

Edition details

Title
  • Parametric sensitivity: A case study comparison.
Appears In
  • Computational Statistics and Data Analysis, v.53, no.7, 2009 May 15, p.2640(13) (ISSN: 0167-9473)
Author
  • Sulieman, H.
  • Kucuk, I.
  • Mclellan, P.J.
Published
  • Elsevier B.V., 2009 May 15
Physical Description
  • Magazine/​Journal article
Subjects
Target Audience
  • Academic
Summary
  • To link to full-text access for this article, visit this link: http:/​/​dx.doi.org/​10.1016/​j.csda.2009.01.003 Byline: H. Sulieman (a), I. Kucuk (a), P.J. McLellan (b) Abstract: This article presents a comparative analysis of three derivative-based parametric sensitivity approaches in multi-response regression estimation: marginal sensitivity, profile-based approach developed by [Sulieman, H., McLellan, P.J., Bacon, D.W., 2004, A Profile-based approach to parametric sensitivity in multiresponse regression models, Computational Statistics & Data Analysis, 45, 721-740] and the commonly used approach of the Fourier Amplitude Sensitivity Test (FAST). We apply the classical formulation of FAST in which Fourier sine coefficients are utilized as sensitivity measures. Contrary to marginal sensitivity, profile-based and FAST approaches provide sensitivity measures that account for model nonlinearity and are pertinent to linear and nonlinear regression models. However, the primary difference between FAST and profile-based sensitivity is that traditional FAST fails to account for parameter dependencies in the model system while these dependencies are considered in the analysis procedure of profile-based sensitivity through the re-estimation of the remaining model parameters conditional on the values of the parameter of interest. An example is discussed to illustrate the comparisons by applying the three sensitivity methods to a model described by set of non-linear differential equations. Some computational aspects are also explored. Author Affiliation: (a) Department of Mathematics and Statistics, American University of Sharjah, P.O.Box 26666, Sharjah, United Arab Emirates (b) Department of Chemical Engineering, Queen's University, Kingston, Ontario, Canada, K7L 3N6 Article History: Received 31 March 2008; Revised 4 January 2009; Accepted 8 January 2009
Terms of Use
  • COPYRIGHT 2009 Elsevier B.V.
Language
  • English
Database
  • Gale Academic OneFile
Identifier
  • 350551128

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