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  1. Bayesian and maximin optimal designs for heteroscedastic regression models
    Autor*in: Dette, Holger; Haines, Linda M.; Imhof, Lorens A.
    Erschienen: 2003
    Verlag:  Univ., SFB 475, Dortmund

    Standort:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Signatur:
    RR 8460(2003,36)
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    W 1490 (2003.36)
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Druck
    Schriftenreihe: Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,36
    Schlagworte: Regressionsanalyse; Modellierung; Theorie; Heteroskedastizität
    Umfang: 23 S
    Bemerkung(en):
  2. Maximin and Bayesian optimal designs for regression models
    Autor*in: Dette, Holger; Haines, Linda M.; Imhof, Lorens A.
    Erschienen: 2003
    Verlag:  Univ., SFB 475, Dortmund

    Standort:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Signatur:
    RR 8460(2003,10)
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    W 1490 (2003.10)
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Druck
    Schriftenreihe: Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,10
    Schlagworte: Regressionsanalyse; Schätztheorie; Theorie
    Umfang: 15 S
    Bemerkung(en):
  3. Maximin and Bayesian optimal designs for linear and non-linear regression models
    Autor*in: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Erschienen: 2002
    Verlag:  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... mehr

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 35 (2002,34)
    Fernleihe:
    keine Fernleihe

     

    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 in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Volltext (kostenfrei)
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/77166
    Schriftenreihe: [Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund ; 2002,34]
    Umfang: Online-Ressource (31 S.)
  4. Maximin and Bayesian optimal designs for regression models
    Autor*in: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Erschienen: 2003
    Verlag:  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... mehr

    Zugang:
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 35 (2003,10)
    Fernleihe:
    keine Fernleihe

     

    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 in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/49339
    Schriftenreihe: [Technical Report / Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund ; 2003,10]
    Umfang: Online-Ressource (15 S.)
  5. Bayesian and maximin optimal designs for heteroscedastic regression models
    Autor*in: Dette, Holger; Haines, Linda M.; Imhof, Lorens
    Erschienen: 2003
    Verlag:  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... mehr

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 35 (2003,36)
    Fernleihe:
    keine Fernleihe

     

    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.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Volltext (kostenfrei)
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/49353
    Schriftenreihe: [Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen ; 2003,36]
    Umfang: Online-Ressource (23 S.)