Готові списки джерел за темами / Parameter estimability

Добірка наукової літератури з теми "Parameter estimability"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Parameter estimability"

1

Jacquez, John A., and Peter Greif. "Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design." Mathematical Biosciences 77, no. 1-2 (December 1985): 201–27. http://dx.doi.org/10.1016/0025-5564(85)90098-7.

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2

Dutta, Sandeep, and William F. Ebling. "Parameter Estimability of Biphasic Response Models." Journal of Pharmaceutical Sciences 86, no. 1 (January 1997): 44–51. http://dx.doi.org/10.1021/js960248f.

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3

Dansie, B. R. "Parameter estimability in the multinomial probit model." Transportation Research Part B: Methodological 19, no. 6 (December 1985): 526–28. http://dx.doi.org/10.1016/0191-2615(85)90047-5.

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4

Fries, R. H., and N. K. Cooperrider. "Bayesian Estimation of Transit Rail Vehicle Parameters." Journal of Dynamic Systems, Measurement, and Control 107, no. 2 (June 1, 1985): 151–58. http://dx.doi.org/10.1115/1.3149687.

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Анотація:
Bayesian parameter estimation is a powerful and versatile method with good convergence properties. This paper describes the use of the Bayesian method to estimate the parameters of a transit rail vehicle. The paper also addresses several issues of general interest in estimation work: cost function formulation, signal processing methods, parameter linking, and parameter estimability.
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5

Fu, Caihong, and Terrance J. Quinn II. "Estimability of natural mortality and other population parameters in a length-based model: Pandalus borealis in Kachemak Bay, Alaska." Canadian Journal of Fisheries and Aquatic Sciences 57, no. 12 (December 1, 2000): 2420–32. http://dx.doi.org/10.1139/f00-220.

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Анотація:
Because stock-assessment models have become more complex, the question of estimability of population parameters is important. We conducted simulation-estimation experiments with a length-based model of the pink shrimp (Pandalus borealis Krøyer) population in Kachemak Bay, Alaska, to compare the robustness of various modeling approaches to specifications of natural mortality (M), catchability (q), a survey gear selectivity parameter (L50), and data imprecision. Our goal was to determine the estimability of various parameters, particularly M, q, and L50. Simulation results suggest that interannual variations in M and L50 could be estimated, if the underlying values have a trend over time. However, parameter q should be fixed at a chosen value, even when it varies over time. Estimated M for the Kachemak Bay P. borealis population increased in the 1980s, perhaps owing to increased predation mortality by groundfish.
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6

Peacock, Stephanie Jane, Martin Krkošek, Mark Alun Lewis, and Subhash Lele. "Study design and parameter estimability for spatial and temporal ecological models." Ecology and Evolution 7, no. 2 (December 30, 2016): 762–70. http://dx.doi.org/10.1002/ece3.2618.

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7

Nogueira, Idelfonso B. R., and Karen V. Pontes. "Parameter estimation with estimability analysis applied to an industrial scale polymerization process." Computers & Chemical Engineering 96 (January 2017): 75–86. http://dx.doi.org/10.1016/j.compchemeng.2016.10.013.

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8

Jiang, Bo, Ming Liu, and Yongge Tian. "On two correlated linear models with common and different parameters." Mathematica Slovaca 72, no. 5 (October 1, 2022): 1355–74. http://dx.doi.org/10.1515/ms-2022-0092.

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Анотація:
Abstract A parametric regression model may comprise several correlated individual regression equations, and these equations may have common and different unknown parameters. In such a situation, the common unknown parameter vectors in these equations can be estimated individually or simultaneously according to various available statistical inference methods. The purpose of this paper is to provide an integral account of two classic objects in regression theory: the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) on common and different unknown parameters in two correlated linear models with common and different parameters. We first introduce some reduced models associated with the two correlated linear models. We then define and characterize predictability and estimability of all unknown parameter vectors in the two correlated models and their reduced models, and derive analytical formulas for calculating the BLUPs and BLUEs of all unknown parameter vectors in these models by means of a constrained quadratic matrix optimization method. We also discuss a variety of theoretical properties of the predictors and estimators.
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9

Bouchkira, Ilias, Abderrazak M. Latifi, Lhachmi Khamar, and Saad Benjelloun. "Global sensitivity based estimability analysis for the parameter identification of Pitzer’s thermodynamic model." Reliability Engineering & System Safety 207 (March 2021): 107263. http://dx.doi.org/10.1016/j.ress.2020.107263.

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10

Yao, K. Zhen, Benjamin M. Shaw, Bo Kou, Kim B. McAuley, and D. W. Bacon. "Modeling Ethylene/Butene Copolymerization with Multi‐site Catalysts: Parameter Estimability and Experimental Design." Polymer Reaction Engineering 11, no. 3 (January 9, 2003): 563–88. http://dx.doi.org/10.1081/pre-120024426.

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Більше джерел

Дисертації з теми "Parameter estimability"

1

Shams, Shiva. "Developing a kinetic model for hydroconversion processing of vacuum residue." Master's thesis, 2011. http://hdl.handle.net/10048/1902.

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Анотація:
One of heavy oils upgrading processes is hydroconversion. As it is a complex process involving many chemical reactions, the mathematical model of hydroconversion process often has more kinetic parameters than can be estimated from the data. In this thesis, a model for hydroconversion processing of vacuum residue is proposed. It is proved that the model is structurally identifiable, but shown that it is inestimable and good parameter estimates may be impossible to obtain even if the model fit is good. As a proof to the model inestimability, it is shown that literature data can be fitted using a subset of only three (of seven) parameters. To improve parameter estimability, a method is proposed for designing additional experiments. The method is based on designing experiments that provide data that is complementary (in an appropriate sense) to existing data. The approach is illustrated using the hydroconversion model. For the hydroconversion model, using two additional experiments provides a good balance between parameter estimation and experimental effort.
Process Control
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2

McLean, Kevin. "Obtaining the Best Model Predictions and Parameter Estimates Using Limited Data." Thesis, 2011. http://hdl.handle.net/1974/6757.

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Анотація:
Engineers who develop fundamental models for chemical processes are often unable to estimate all of the model parameters due to problems with parameter identifiability and estimability. The literature concerning these two concepts is reviewed and techniques for assessing parameter identifiability and estimability in nonlinear dynamic models are summarized. Modellers often face estimability problems when the available data are limited or noisy. In this situation, modellers must decide whether to conduct new experiments, change the model structure, or to estimate only a subset of the parameters and leave others at fixed values. Estimating only a subset of important model parameters is a technique often used by modellers who face estimability problems and it may lead to better model predictions with lower mean squared error (MSE) than the full model with all parameters estimated. Different methods in the literature for parameter subset selection are discussed and compared. An orthogonalization algorithm combined with a recent MSE-based criterion has been used successfully to rank parameters from most to least estimable and to determine the parameter subset that should be estimated to obtain the best predictions. In this work, this strategy is applied to a batch reactor model using additional data and results are compared with computationally-expensive leave-one-out cross-validation. A new simultaneous ranking and selection technique based on this MSE criterion is also described. Unfortunately, results from these parameter selection techniques are sensitive to the initial parameter values and the uncertainty factors used to calculate sensitivity coefficients. A robustness test is proposed and applied to assess the sensitivity of the selected parameter subset to the initial parameter guesses. The selected parameter subsets are compared with those selected using another MSE-based method proposed by Chu et al. (2009). The computational efforts of these methods are compared and recommendations are provided to modellers.
Thesis (Master, Chemical Engineering) -- Queen's University, 2011-09-27 10:52:31.588
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3

Thompson, Duncan. "Modeling of Molecular Weight Distributions in Ziegler-Natta Catalyzed Ethylene Copolymerizations." Thesis, 2009. http://hdl.handle.net/1974/1896.

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Анотація:
The objective of this work is to develop mathematical models to predict molecular weight distributions (MWDs) of ethylene copolymers produced in an industrial gas-phase reactor using a Ziegler-Natta (Z-N) catalyst. Because of the multi-site nature of Z-N catalysts, models of Z-N catalyzed copolymerization tend to be very large and have many parameters that need to be estimated. It is important that the data that are available for parameter estimation be used effectively, and that a suitable balance is achieved between modeling rigour and simplification. In the thesis, deconvolution analysis is used to gain an understanding of how the polymer produced by various types of active sites on the Z-N catalyst responds to changes in the reactor operating conditions. This analysis reveals which reactions are important in determining the MWD and also shows that some types of active sites share similar behavior and can therefore share some kinetic parameters. With this knowledge, a simplified model is developed to predict MWDs of ethylene/hexene copolymers produced at 90 °C. Estimates of the parameters in this isothermal model provide good initial guesses for parameter estimation in a subsequent more complex model. The isothermal model is extended to account for the effects of butene and temperature. Estimability analysis and cross-validation are used to determine which parameters should be estimated from the available industrial data set. Twenty model parameters are estimated so that the model provides good predictions of MWD and comonomer incorporation. Finally, D-, A-,and V-optimal experimental designs for improving the quality of the model predictions are determined. Difficulties with local minima are addressed and a comparison of the optimality criteria is presented.
Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2009-05-28 20:43:58.37
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Частини книг з теми "Parameter estimability"

1

Korner-Nievergelt, Fränzi, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt. "Prior Influence and Parameter Estimability." In Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN, 265–78. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-801370-0.00015-0.

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Тези доповідей конференцій з теми "Parameter estimability"

1

Wei, Erhu, and Qi Zhang. "On the Estimability of Geodetic Parameters of Mars with Pulsar Observations." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5629660.

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2

Wei, Erhu, Jingnan Liu, Wei Yan, and Chuang Shi. "On the estimability of geodetic parameters with space-ground and space-space SVLBI observations." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.816216.

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3

Guillaume, Sicot, Ghannami Mohamed Ali, Lennon Marc, and Loyer Sophie. "Estimability Study of the Parameters of the Semi-Analytical Lee Model with Hyperspectral Data." In 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2021. http://dx.doi.org/10.1109/whispers52202.2021.9483991.

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