A Bayesian Study of the Error-in-Variables Model

Reilly, Park M.; Patino‐Leal, Hugo

doi:10.2307/1267784

Cited by 143 publications

(117 citation statements)

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“…A full statistical explanation of the error-in-variables-model (and enumeration of its benefits) has been presented previously; the interested reader should refer to Reilly and Patino-Leal [7] or Kazemi et al [3,6,8]. Only the basics are presented herein, for the reader to have a brief overview before we tackle the case studies.…”

Section: Error-in-variables-model (Evm)mentioning

confidence: 99%

“…This information is required to relate weight conversion data (X w , which can be experimentally determined using gravimetry) to molar conversion data (X n , which is used in the DNI as per Equations (2) and (3)). The program converts X w to X n according to Equation (7).…”

Section: Cumulative Modelmentioning

confidence: 99%

“…A detailed discussion of the nested-iterative EVM algorithm has been presented by Reilly and Patino-Leal [7] and has more recently been described by Kazemi et al [3,6]. Equation (A9) below is the definition of G, the second derivative of Φ with respect to the parameters, given earlier by Equation (8) (Section 3.3).…”

Section: Appendix A2 Calculation Of G As An Evm Program Outputmentioning

confidence: 99%

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Computational Package for Copolymerization Reactivity Ratio Estimation: Improved Access to the Error-in-Variables-Model

Scott

Penlidis

2018

Processes

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Abstract:The error-in-variables-model (EVM) is the most statistically correct non-linear parameter estimation technique for reactivity ratio estimation. However, many polymer researchers are unaware of the advantages of EVM and therefore still choose to use rather erroneous or approximate methods. The procedure is straightforward but it is often avoided because it is seen as mathematically and computationally intensive. Therefore, the goal of this work is to make EVM more accessible to all researchers through a series of focused case studies. All analyses employ a MATLAB-based computational package for copolymerization reactivity ratio estimation. The basis of the package is previous work in our group over many years. This version is an improvement, as it ensures wider compatibility and enhanced flexibility with respect to copolymerization parameter estimation scenarios that can be considered.

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Section: Error-in-variables-model (Evm)mentioning

confidence: 99%

Section: Cumulative Modelmentioning

confidence: 99%

Section: Appendix A2 Calculation Of G As An Evm Program Outputmentioning

confidence: 99%

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Computational Package for Copolymerization Reactivity Ratio Estimation: Improved Access to the Error-in-Variables-Model

Scott

Penlidis

2018

Processes

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“…Later on, Britt and Luecke [9] suggested a general algorithm based on the concept of Lagrange multipliers. Peneloux et al [10] and Reilly and Patino-Leal [11] provided computational improvements. Schwetlick and Tiller [12] separated the parameter estimation and data reconciliation steps and Valk !…”

Section: Eiv Methodsmentioning

confidence: 99%

Error-in-variables for rock failure envelope

Zambrano-Mendoza

Russell

2003

International Journal of Rock Mechanics and Mining Sciences

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“…Various approaches for minimising this error have been proposed -see, for instance, Reilly and Patino-Leal (1981), Wan (1990), Freund and Schapire (1997), and Mwitondi et al (2002). A commonly acceptable practice is to vary the allocation rule in order to address specific requirements of an application.…”

Section: Allocation Rule Errors Due To Data Randomnessmentioning

confidence: 99%