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Bayesian and Nonparametric Bayesian Optimal Designs With the Length of Interconnected Intervals for Unit Exponential Regression Model.

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Title: Bayesian and Nonparametric Bayesian Optimal Designs With the Length of Interconnected Intervals for Unit Exponential Regression Model.
Authors: Khazaei, Soleiman1 (AUTHOR) soleimank@gmail.com, Abdollahi Nanvapisheh, Anita1 (AUTHOR), Jafari, Habib1 (AUTHOR), Sen, Smritijit1 (AUTHOR) smsen@wiley.com
Source: Journal of Probability & Statistics. 7/9/2026, Vol. 2026, p1-11. 11p.
Subjects: Optimal designs (Statistics), Bayesian analysis, Regression analysis, Nonlinear regression
Abstract: Nonlinear regression models are widely utilized in many scientific disciplines. Precisely estimating an optimal nonlinear regression model is critical, especially when considering potential biases may arise in Bayesian optimal design. The current work introduces Bayesian and nonparametric Bayesian optimal designs based on the length of interconnected intervals for the unit exponential (UE) regression model. In cases where parameter information or historical data are limited, a nonparametric Bayesian method is employed through placing a Dirichlet process (DP) prior on the space of distribution functions. Lastly, the efficiency of various optimal designs is assessed and compared. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Probability+%26+Statistics%22">Journal of Probability & Statistics</searchLink>. 7/9/2026, Vol. 2026, p1-11. 11p.
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  Data: Nonlinear regression models are widely utilized in many scientific disciplines. Precisely estimating an optimal nonlinear regression model is critical, especially when considering potential biases may arise in Bayesian optimal design. The current work introduces Bayesian and nonparametric Bayesian optimal designs based on the length of interconnected intervals for the unit exponential (UE) regression model. In cases where parameter information or historical data are limited, a nonparametric Bayesian method is employed through placing a Dirichlet process (DP) prior on the space of distribution functions. Lastly, the efficiency of various optimal designs is assessed and compared. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/jpas/8892090
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Nonlinear regression
        Type: general
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      – TitleFull: Bayesian and Nonparametric Bayesian Optimal Designs With the Length of Interconnected Intervals for Unit Exponential Regression Model.
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              Text: 7/9/2026
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              Y: 2026
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