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Simultaneous Optimization of Multiple Responses with the R Package JOP

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  • Kuhnt, Sonja
  • Rudak, Nikolaus

Abstract

A joint optimization plot, shortly JOP, graphically displays the result of a loss function based robust parameter design for multiple responses. Different importance of reaching a target value can be assigned to the individual responses by weights. The JOP method simultaneously runs through a whole range of possible weights. For each weight matrix a parameter setting is derived which minimizes the estimated expected loss. The joint optimization plot displays these settings together with corresponding expected values and standard deviations of the response variable. The R package JOP provides all tools necessary to apply the JOP approach to a given data set. It also returns parameter settings for a desirable compromise of achieved expected responses chosen from the plot.

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  • Kuhnt, Sonja & Rudak, Nikolaus, 2013. "Simultaneous Optimization of Multiple Responses with the R Package JOP," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i09).
    • Handle: RePEc:jss:jstsof:v:054:i09
    DOI: http://hdl.handle.net/10.18637/jss.v054.i09
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    References listed on IDEAS

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    1. Murray Aitkin, 1987. "Modelling Variance Heterogeneity in Normal Regression Using GLIM," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 332-339, November.
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    Cited by:

    1. Holger Dette & Laura Hoyden & Sonja Kuhnt & Kirsten Schorning, 2017. "Optimal designs for thermal spraying," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 53-72, January.

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