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Displaying results 1 to 5 of 5.

  1. Maximin and Bayesian optimal designs for regression models
    Author: Dette, Holger; Haines, Linda M.; Imhof, Lorens A.
    Published: 2003
    Publisher:  Univ., SFB 475, Dortmund

    Location:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Signature:
    RR 8460(2003,10)
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    W 1490 (2003.10)
    Inter-library loan:
    Unlimited inter-library loan, copies and loan
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    Series: Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,10
    Subjects: Regressionsanalyse; Schätztheorie; Theorie
    Scope: 15 S
    Notes:
  2. Bayesian and maximin optimal designs for heteroscedastic regression models
    Author: Dette, Holger; Haines, Linda M.; Imhof, Lorens A.
    Published: 2003
    Publisher:  Univ., SFB 475, Dortmund

    Location:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Signature:
    RR 8460(2003,36)
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    W 1490 (2003.36)
    Inter-library loan:
    Unlimited inter-library loan, copies and loan
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    Series: Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,36
    Subjects: Regressionsanalyse; Modellierung; Theorie; Heteroskedastizität
    Scope: 23 S
    Notes:
  3. Bayesian and maximin optimal designs for heteroscedastic regression models
    Author: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Published: 2003
    Publisher:  Univ., SFB 475, Dortmund

    The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by... more

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 35 (2003,36)
    Inter-library loan:
    No inter-library loan

     

    The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs can be obtained as weak limits of the corresponding Bayesian Φq-optimal designs. The approach is illustrated for two specific weighted polynomial models and also for a particular growth model.

     
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    Content information
    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/49353
    Series: [Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,36]
    Scope: Online-Ressource (23 S.)
  4. Maximin and Bayesian optimal designs for regression models
    Author: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Published: 2003
    Publisher:  Univ., SFB 475, Dortmund

    For many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the... more

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    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 35 (2003,10)
    Inter-library loan:
    No inter-library loan

     

    For many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the context of design is to construct maximin optimal designs as those designs which maximize the minimum value of a real valued (standardized) function of the Fisher information matrix, where the minimum is taken over a specified range of the unknown parameters. The maximin criterion is not differentiable and the construction of the associated optimal designs is therefore difficult to achieve in practice. In the present paper the relationship between maximin optimal designs and a class of Bayesian optimal designs for which the associated criteria are differentiable is explored. In particular, a general methodology for determining maximin optimal designs is introduced based on the fact that in many cases these designs can be obtained as weak limits of appropriate Bayesian optimal designs.

     
    Export to reference management software   RIS file
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    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/49339
    Series: [Technical Report / Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund ; 2003,10]
    Scope: Online-Ressource (15 S.)
  5. Maximin and Bayesian optimal designs for linear and non-linear regression models
    Author: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Published: 2002
    Publisher:  SFB 475, Universität Dortmund, Dortmund

    For many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the... more

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 35 (2002,34)
    Inter-library loan:
    No inter-library loan

     

    For many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the context of design is to construct maximin optimal designs as those designs which maximize the minimum value of a real valued (standardized) function of the Fisher information matrix, where the minimum is taken over a specified range of the unknown parameters. The maximin criterion is not differentiable and the construction of the associated optimal designs is therefore difficult to achieve in practice. In the present paper the relationship between maximin optimal designs and a class of Bayesian optimal designs for which the associated criteria are differentiable is explored. In particular, a general methodology for determining maximin optimal designs is introduced based on the fact that in many cases these designs can be obtained as weak limits of appropriate Bayesian optimal designs. The approach is illustrated by means of a broad range of examples for which the Bayesian optimal and hence the maximin optimal designs can be found explicitly.

     
    Export to reference management software   RIS file
      BibTeX file
    Content information
    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/77166
    Series: [Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund ; 2002,34]
    Scope: Online-Ressource (31 S.)