A Bayesian Adjustment for Multiplicative Measurement Errors for a Calibration Problem with Application to a Stem Cell Study

Zhang, Peng; Liu, Juxin; Dong, Jianghu; Holovati, Jelena L.; Letcher, Brenda; McGann, Locksley E.

doi:10.1111/j.1541-0420.2011.01641.x

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The proposed model assumes a lognormal-based multiplicative Berkson-type measurement error in the concentrations, which can be viewed as “explanatory variables” from a regression modeling standpoint. To the best of the authors’ knowledge, the model structure of Fig 1 is novel—it is different from well-established statistical procedures [ 25 , 27 ] that focus on classical measurement error, and it is also different from the literature on additive Berkson-type measurement error [ 28 , 29 ] and prior work on bounded multiplicative Berkson-type measurement errors [ 30 ].…”

Section: Bayesian Inference For Light Scattering Datamentioning

confidence: 99%

Bayesian analysis of static light scattering data for globular proteins

et al. 2021

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Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution. The second virial coefficient is a physical quantity that characterizes the magnitude and sign of pairwise interactions between particles, and hence is related to aggregation propensity, a property of considerable scientific and practical interest. Estimating the second virial coefficient from experimental data is challenging due both to the degree of precision required and the complexity of the error structure involved. In contrast to conventional approaches based on heuristic ordinary least squares estimates, Bayesian inference for the second virial coefficient allows explicit modeling of error processes, incorporation of prior information, and the ability to directly test competing physical models. Here, we introduce a fully Bayesian model for static light scattering experiments on small-particle systems, with joint inference for concentration, index of refraction, oligomer size, and the second virial coefficient. We apply our proposed model to study the aggregation behavior of hen egg-white lysozyme and human γS-crystallin using in-house experimental data. Based on these observations, we also perform a simulation study on the primary drivers of uncertainty in this family of experiments, showing in particular the potential for improved monitoring and control of concentration to aid inference.

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Section: Bayesian Inference For Light Scattering Datamentioning

confidence: 99%

Bayesian analysis of static light scattering data for globular proteins

et al. 2021

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show abstract

“…The proposed model assumes a lognormal-based multiplicative Berkson-type measurement error in the concentrations, which can be viewed as "explanatory variables" from a regression modeling standpoint. To the best of the authors' knowledge, this model structure is novel -it is different from well-established statistical procedures (Hwang, 1986;Carroll et al, 2006) that focus on classical measurement error, and it is also different from the literature on additive Berkson-type measurement error (Rudemo et al, 1989;Muff et al, 2015) and prior work on bounded multiplicative Berkson-type measurement errors (Zhang et al, 2012).…”

Section: Connections With Other Modelsmentioning

confidence: 99%

Bayesian Analysis of Static Light Scattering Data for Globular Proteins

Yin,

Khago,

Martin

et al. 2020

Preprint

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Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution. The second virial coefficient is a physical quantity that characterizes the magnitude and sign of pairwise interactions between particles, and hence is related to aggregation propensity, a property of considerable scientific and practical interest. Estimating the second virial coefficient from experimental data is challenging due both to the degree of precision required and the complexity of the error structure involved. In contrast to conventional approaches based on heuristic OLS estimates, Bayesian inference for the second virial coefficient allows explicit modeling of error processes, incorporation of prior information, and the ability to directly test competing physical models. Here, we introduce a fully Bayesian model for static light scattering experiments on small-particle systems, with joint inference for concentration, index of refraction, oligomer size, and the second virial coefficient. We apply our proposed model to study the aggregation behavior of hen egg-white lysozyme and human γS-crystallin using in-house experimental data. Based on these observations, we also perform a simulation study on the primary drivers of uncertainty in this family of experiments, showing in particular the potential for improved monitoring and control of concentration to aid inference.

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Structural Uncertainty and Pollution Control: Optimal Stringency with Unknown Pollution Sources

Carson

LaRiviere

2017

Environ Resource Econ

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A Bayesian Adjustment for Multiplicative Measurement Errors for a Calibration Problem with Application to a Stem Cell Study

Cited by 4 publications

References 18 publications

Bayesian analysis of static light scattering data for globular proteins

Bayesian analysis of static light scattering data for globular proteins

Bayesian Analysis of Static Light Scattering Data for Globular Proteins

Structural Uncertainty and Pollution Control: Optimal Stringency with Unknown Pollution Sources

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