Relevant bibliographies by topics / Models of generalized estimating equations

Academic literature on the topic 'Models of generalized estimating equations'

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Published: 25 May 2024

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Journal articles on the topic "Models of generalized estimating equations"

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Vens, M., and A. Ziegler. "Generalized Estimating Equations." Methods of Information in Medicine 49, no. 05 (2010): 421–25. http://dx.doi.org/10.3414/me10-01-0026.

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Summary Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure. Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE. Methods: We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables. Results: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient. Conclusions: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.
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Feddag, Mohand-Larbi, Ion Grama, and Mounir Mesbah. "Generalized Estimating Equations (GEE) for Mixed Logistic Models." Communications in Statistics - Theory and Methods 32, no. 4 (January 4, 2003): 851–74. http://dx.doi.org/10.1081/sta-120018833.

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Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 02 (November 2007): 323–43. http://dx.doi.org/10.2143/ast.37.2.2024070.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.
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Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 2 (November 2007): 323–43. http://dx.doi.org/10.1017/s0515036100014896.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.
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Breitung, J., N. R. Chaganty, R. M. Daniel, M. G. Kenward, M. Lechner, P. Martus, R. T. Sabo, Y. G. Wang, and C. Zorn. "Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”." Methods of Information in Medicine 49, no. 05 (2010): 426–32. http://dx.doi.org/10.1055/s-0038-1625133.

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Summary Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods: Inviting an international group of experts to comment on this paper. Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspects in data analysis. Conclusions: In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data. This particularly applies to the situation when data are missing at random.
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Zubair, Seema, and Sanjoy K. Sinha. "Marginal models for longitudinal count data with dropouts." Journal of Statistical Research 54, no. 1 (August 25, 2020): 27–42. http://dx.doi.org/10.47302/jsr.2020540102.

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In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Study (HRS) (HRS, 2019)
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Ma, Yanyuan, and Marc G. Genton. "Explicit estimating equations for semiparametric generalized linear latent variable models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 4 (July 5, 2010): 475–95. http://dx.doi.org/10.1111/j.1467-9868.2010.00741.x.

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Corrente, JosÉ Eduardo, and Maria Del Pilar DÍAz. "Ordinal models and generalized estimating equations to evaluate disease severity." Journal of Applied Statistics 30, no. 4 (May 2003): 425–39. http://dx.doi.org/10.1080/0266476032000035458.

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Koper, Nicola, and Micheline Manseau. "Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection." Journal of Applied Ecology 46, no. 3 (June 2009): 590–99. http://dx.doi.org/10.1111/j.1365-2664.2009.01642.x.

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Nikita, Efthymia. "The use of generalized linear models and generalized estimating equations in bioarchaeological studies." American Journal of Physical Anthropology 153, no. 3 (December 13, 2013): 473–83. http://dx.doi.org/10.1002/ajpa.22448.

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Dissertations / Theses on the topic "Models of generalized estimating equations"

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Alnaji, Lulah A. "Generalized Estimating Equations for Mixed Models." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530292694012892.

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Huang, Danwei. "Robustness of generalized estimating equations in credibility models." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38842312.

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Huang, Danwei, and 黃丹薇. "Robustness of generalized estimating equations in credibility models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38842312.

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Cai, Jianwen. "Generalized estimating equations for censored multivariate failure time data /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/9581.

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Jang, Mi Jin. "Working correlation selection in generalized estimating equations." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2719.

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Longitudinal data analysis is common in biomedical research area. Generalized estimating equations (GEE) approach is widely used for longitudinal marginal models. The GEE method is known to provide consistent regression parameter estimates regardless of the choice of working correlation structure, provided the square root of n consistent nuisance parameters are used. However, it is important to use the appropriate working correlation structure in small samples, since it improves the statistical efficiency of β estimate. Several working correlation selection criteria have been proposed (Rotnitzky and Jewell, 1990; Pan, 2001; Hin and Wang, 2009; Shults et. al, 2009). However, these selection criteria have the same limitation in that they perform poorly when over-parameterized structures are considered as candidates. In this dissertation, new working correlation selection criteria are developed based on generalized eigenvalues. A set of generalized eigenvalues is used to measure the disparity between the bias-corrected sandwich variance estimator under the hypothesized working correlation matrix and the model-based variance estimator under a working independence assumption. A summary measure based on the set of the generalized eigenvalues provides an indication of the disparity between the true correlation structure and the misspecified working correlation structure. Motivated by the test statistics in MANOVA, three working correlation selection criteria are proposed: PT (Pillai's trace type criterion),WR (Wilks' ratio type criterion) and RMR (Roy's Maximum Root type criterion). The relationship between these generalized eigenvalues and the CIC measure is revealed. In addition, this dissertation proposes a method to penalize for the over-parameterized working correlation structures. The over-parameterized structure converges to the true correlation structure, using extra parameters. Thus, the true correlation structure and the over-parameterized structure tend to provide similar variance estimate of the estimated β and similar working correlation selection criterion values. However, the over-parameterized structure is more likely to be chosen as the best working correlation structure by "the smaller the better" rule for criterion values. This is because the over-parameterization leads to the negatively biased sandwich variance estimator, hence smaller selection criterion value. In this dissertation, the over-parameterized structure is penalized through cluster detection and an optimization function. In order to find the group ("cluster") of the working correlation structures that are similar to each other, a cluster detection method is developed, based on spacings of the order statistics of the selection criterion measures. Once a cluster is found, the optimization function considering the trade-off between bias and variability provides the choice of the "best" approximating working correlation structure. The performance of our proposed criterion measures relative to other relevant criteria (QIC, RJ and CIC) is examined in a series of simulation studies.
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Clark, Seth K. "Model Robust Regression Based on Generalized Estimating Equations." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/26588.

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One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a nonparametric fit. The combined predictor can have less bias than the parametric model estimate alone and less variance than the nonparametric estimate alone. Additionally, as shown in previous work for uncorrelated data with linear mean function, MRR can converge faster than the nonparametric predictor alone. We extend the MRR technique to the problem of predicting mean response for clustered non-normal data. We combine a nonparametric method based on local estimation with a global, parametric generalized estimating equations (GEE) estimate through a mixing parameter on both the mean scale and the linear predictor scale. As a special case, when data are uncorrelated, this amounts to mixing a local likelihood estimate with predictions from a global generalized linear model. Cross-validation bandwidth and optimal mixing parameter selectors are developed. The global fits and the optimal and data-driven local and mixed fits are studied under no/some/substantial model misspecification via simulation. The methods are then illustrated through application to data from a longitudinal study.
Ph. D.
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Akanda, Md Abdus Salam. "A generalized estimating equations approach to capture-recapture closed population models: methods." Doctoral thesis, Universidade de Évora, 2014. http://hdl.handle.net/10174/18297.

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ABSTRACT; Wildlife population parameters, such as capture or detection probabilities, and density or population size, can be estimated from capture-recapture data. These estimates are of particular interest to ecologists and biologists who rely on ac- curate inferences for management and conservation of the population of interest. However, there are many challenges to researchers for making accurate inferences on population parameters. For instance, capture-recapture data can be considered as binary longitudinal observations since repeated measurements are collected on the same individuals across successive points in times, and these observations are often correlated over time. If these correlations are not taken into account when estimating capture probabilities, then parameter estimates will be biased, possibly producing misleading results. Also, an estimator of population size is generally biased under the presence of heterogeneity in capture probabilities. The use of covariates (or auxiliary variables), when available, has been proposed as an alternative way to cope with the problem of heterogeneous capture probabilities. In this dissertation, we are interested in tackling these two main problems, (i) when capture probabilities are dependent among capture occasions in closed population capture-recapture models, and (ii) when capture probabilities are heterogeneous among individuals. Hence, the capture-recapture literature can be improved, if we could propose an approach to jointly account for these problems. In summary, this dissertation proposes: (i) a generalized estimating equations (GEE) approach to model possible effects in capture-recapture closed population studies due to correlation over time and individual heterogeneity; (ii) the corresponding estimating equations for each closed population capture-recapture model; (iii) a comprehensive analysis on various real capture-recapture data sets using classical, GEE and generalized linear mixed models (GLMM); (iv) an evaluation of the effect of ac- counting for correlation structures on capture-recapture model selection based on the ‘Quasi-likelihood Information Criterion (QIC)’; (v) a comparison of the performance of population size estimators using GEE and GLMM approaches in the analysis of capture-recapture data. The performance of these approaches is evaluated by Monte Carlo (MC) simulation studies resembling real capture-recapture data. The proposed GEE approach provides a useful inference procedure for estimating population parameters, particularly when a large proportion of individuals are captured. For a low capture proportion, it is difficult to obtain reliable estimates for all approaches, but the GEE approach outperforms the other methods. Simulation results show that quasi-likelihood GEE provide lower standard error than partial likelihood based on generalized linear modelling (GLM) and GLMM approaches. The estimated population sizes vary on the nature of the existing correlation among capture occasions; RESUMO: Parâmetros populacionais em espécies de vida selvagens, como probabilidade captura ou deteção, e abundância ou densidade da população, podem ser estimados a partir de dados de captura-recaptura. Estas estimativas são de particular interesse para ecologistas e biólogos que dependem de inferências precisas a gestão e conservação das populações. No entanto, há muitos desafios par investigadores fazer inferências precisas de parâmetros populacionais. Por exemplo, os dados de captura-recaptura podem ser considerados como observa longitudinais binárias uma vez que são medições repetidas coletadas nos mesmos indivíduos em pontos sucessivos no tempo, e muitas vezes correlacionadas. Essas correlações não são levadas em conta ao estimar as probabilidades de tura, as estimativas dos parâmetros serão tendenciosas e possivelmente produz resultados enganosos. Também, um estimador do tamanho de uma população geralmente enviesado na presença de heterogeneidade das probabilidades de captura. A utilização de co-variáveis (ou variáveis auxiliares), quando disponível tem sido proposta como uma forma de lidar com o problema de probabilidade captura heterogéneas. Nesta dissertação, estamos interessados em abordar problemas principais em mode1os de captura-recapturar para população fecha (i) quando as probabilidades de captura são dependentes entre ocasiões de captura e (ii) quando as probabilidades de captura são heterogéneas entre os indivíduos Assim, a literatura de captura-recaptura pode ser melhorada, se pudéssemos por uma abordagem conjunta para estes problemas. Em resumo, nesta dissertação propõe-se: (i) uma abordagem de estimação de equações generalizadas (GEE) para modelar possíveis efeitos de correlação temporal e heterogeneidade individual nas probabilidades de captura; (ii) as correspondentes equações de estimação generalizadas para cada modelo de captura-recaptura em população fechadas; (iii) uma análise sobre vários conjuntos de dados reais de captura-recaptura usando a abordagem clássica, GEE e modelos linear generalizados misto (GLMM); (iv) uma avaliação do efeito das estruturas de correlação na seleção de modelos de captura-recaptura com base no ‘critério de informação da Quasi-verossimilhança (QIC); (v) uma comparação da performance das estimativas do tamanho da população usando GEE e GLMM. O desempenho destas abordagens ´e avaliado usando simulações Monte Carlo (MC) que se assemelham a dados de captura- recapture reais. A abordagem GEE proposto ´e um procedimento de inferência útil para estimar parâmetros populacionais, especialmente quando uma grande proporção de indivíduos ´e capturada. Para uma proporção baixa de capturas, ´e difícil obter estimativas fiáveis para todas as abordagens aplicadas, mas GEE supera os outros métodos. Os resultados das simulações mostram que o método da quase-verossimilhança do GEE fornece estimativas do erro padrão menor do que o método da verossimilhança parcial dos modelos lineares generalizados (GLM) e GLMM. As estimativas do tamanho da população variam de acordo com a natureza da correlação existente entre as ocasiões de captura.
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Cao, Jiguo. "Generalized profiling method and the applications to adaptive penalized smoothing, generalized semiparametric additive models and estimating differential equations." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102483.

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Many statistical models involve three distinct groups of variables: local or nuisance parameters, global or structural parameters, and complexity parameters. In this thesis, we introduce the generalized profiling method to estimate these statistical models, which treats one group of parameters as an explicit or implicit function of other parameters. The dimensionality of the parameter space is reduced, and the optimization surface becomes smoother. The Newton-Raphson algorithm is applied to estimate these three distinct groups of parameters in three levels of optimization, with the gradients and Hessian matrices written out analytically by the Implicit Function Theorem it' necessary and allowing for different criteria for each level of optimization. Moreover, variances of global parameters are estimated by the Delta method and include the variation coming from complexity parameters. We also propose three applications of the generalized profiling method.
First, penalized smoothing is extended by allowing for a functional smoothing parameter, which is adaptive to the geometry of the underlying curve, which is called adaptive penalized smoothing. In the first level of optimization, the smooth ing coefficients are local parameters, estimated by minimizing sum of squared errors, conditional on the functional smoothing parameter. In the second level, the functional smoothing parameter is a complexity parameter, estimated by minimizing generalized cross-validation (GCV), treating the smoothing coefficients as explicit functions of the functional smoothing parameter. Adaptive penalized smoothing is shown to obtain better estimates for fitting functions and their derivatives.
Next, the generalized semiparametric additive models are estimated by three levels of optimization, allowing response variables in any kind of distribution. In the first level, the nonparametric functional parameters are nuisance parameters, estimated by maximizing the regularized likelihood function, conditional on the linear coefficients and the smoothing parameter. In the second level, the linear coefficients are structural parameters, estimated by maximizing the likelihood function with the nonparametric functional parameters treated as implicit functions of linear coefficients and the smoothing parameter. In the third level, the smoothing parameter is a complexity parameter, estimated by minimizing the approximated GCV with the linear coefficients treated as implicit functions of the smoothing parameter. This method is applied to estimate the generalized semiparametric additive model for the effect of air pollution on the public health.
Finally, parameters in differential equations (DE's) are estimated from noisy data with the generalized profiling method. In the first level of optimization, fitting functions are estimated to approximate DE solutions by penalized smoothing with the penalty term defined by DE's, fixing values of DE parameters. In the second level of optimization, DE parameters are estimated by weighted sum of squared errors, with the smoothing coefficients treated as an implicit function of DE parameters. The effects of the smoothing parameter on DE parameter estimates are explored and the optimization criteria for smoothing parameter selection are discussed. The method is applied to fit the predator-prey dynamic model to biological data, to estimate DE parameters in the HIV dynamic model from clinical trials, and to explore dynamic models for thermal decomposition of alpha-Pinene.
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Liu, Fangda, and 刘芳达. "Two results in financial mathematics and bio-statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46976437.

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Zheng, Xueying, and 郑雪莹. "Robust joint mean-covariance model selection and time-varying correlation structure estimation for dependent data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899703.

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In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model. Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study.
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Statistics and Actuarial Science
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Doctor of Philosophy
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Books on the topic "Models of generalized estimating equations"

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Gregory, Allan W. Estimating equations with combined moving average error processes under rational expectations. London, Canada: Dept. of Economics, University of Western Ontario, 1985.

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author, Ieno Elena N., ed. Beginner's guide to zero-inflated models with R. Newburgh, United Kingdom: Highland Statistics Ltd., 2016.

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Ziegler, Andreas. Generalized Estimating Equations. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6.

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Joseph, Hilbe, ed. Generalized estimating equations. Boca Raton, FL: Chapman & Hall/CRC, 2003.

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service), SpringerLink (Online, ed. Generalized Estimating Equations. New York, NY: Springer Science+Business Media, LLC, 2011.

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1944-, Hilbe Joseph M., ed. Quasi-least squares regression. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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Jinfang, Wang, ed. Numerical methods for nonlinear estimating equations. Oxford: Clarendon Press, 2003.

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Bayoumi, Tamim A. Estimating trade equations from aggregate bilateral data. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Department, 1999.

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Pseudo Maximum Likelihood Methode und Generalised Estimating Equations zur Analyse korrelierter Daten. Frankfurt am Main: P. Lang, 1999.

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Knafl, George J. Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41988-1.

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Book chapters on the topic "Models of generalized estimating equations"

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Ziegler, Andreas. "Generalized linear models." In Generalized Estimating Equations, 21–28. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_3.

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Bravo, Francesco. "Semiparametric Generalized Estimating Equations in Misspecified Models." In Springer Proceedings in Mathematics & Statistics, 43–52. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0569-0_5.

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Park, Hyoshin, and Nigel Pugh. "Generalized Estimating Equations Model Based Recursive Partitioning: Applied to Distracted Driving." In Advances in Intelligent Systems and Computing, 833–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93885-1_77.

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Ziegler, Andreas, and Maren Vens. "Generalized Estimating Equations." In Handbook of Epidemiology, 1337–76. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-09834-0_45.

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Molenberghs, Geert, Geert Verbeke, and Michael G. Kenward. "Generalized Estimating Equations." In Handbook of Epidemiology, 1–23. New York, NY: Springer New York, 2023. http://dx.doi.org/10.1007/978-1-4614-6625-3_45-1.

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Ziegler, Andreas. "The linear exponential family." In Generalized Estimating Equations, 1–10. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_1.

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Ziegler, Andreas. "The quadratic exponential family." In Generalized Estimating Equations, 11–20. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_2.

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Ziegler, Andreas. "Maximum likelihood method." In Generalized Estimating Equations, 29–49. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_4.

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Ziegler, Andreas. "Pseudo maximum likelihood method based on the linear exponential family." In Generalized Estimating Equations, 51–77. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_5.

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Ziegler, Andreas. "Quasi generalized pseudo maximum likelihood method based on the linear exponential family." In Generalized Estimating Equations, 79–99. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_6.

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Conference papers on the topic "Models of generalized estimating equations"

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Lin, Xinfan, Anna Stefanopoulou, Patricia Laskowsky, Jim Freudenberg, Yonghua Li, and R. Dyche Anderson. "State of Charge Estimation Error due to Parameter Mismatch in a Generalized Explicit Lithium Ion Battery Model." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6193.

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Model-based state of charge (SOC) estimation with output feedback of the voltage error is steadily augmenting more traditional coulomb counting or voltage inversion techniques in hybrid electric vehicle applications. In this paper, the state (SOC) estimation error in the presence of model parameter mismatch is calculated for a general lithium ion battery model with linear diffusion or impedance-based state dynamics and nonlinear output voltage equations. The estimation error due to initial conditions and inputs is derived for linearized battery models and also verified by nonlinear simulations. It is shown that in some cases of parameter mismatch, the state, e.g. SOC, estimation error will be significant while the voltage estimation error is negligible.
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Yang, Qingcai, Yunpeng Cao, Fang Yu, Jianwei Du, and Shuying Li. "Health Estimation of Gas Turbine: A Symbolic Linearization Model Approach." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64071.

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This paper is mainly concerned with the health estimation of a gas turbine using a symbolic linearization model approach. Health parameters will change with the degradation of gas turbine performance. Monitoring and evaluating these health parameters can assist in the development of predictive control techniques and maintenance schedules. Currently, various health parameter estimation methods have been studied extensively, but there have been less related studies on how to obtain statespace models. In this paper, a symbolic linearization model method is presented to overcome the shortcoming of high time consumption suffered by existing methods. In this method, each component model of the dynamic nonlinear gas turbine model is decomposed into several sub-modules, each of which contains a simple nonlinear equation. By means of symbolic computation, a linear model of the components is derived by linearizing these sub-modules, and then the generalized linear state-space model of the gas turbine is derived from the relationship among the components. In the generalized linear state-space model, the Jacobian matrices are functions of the parameters under a steady-state operating condition. Therefore, it is easy to obtain a linear model that represents the dynamics of the gas turbine under a given operating condition. To estimate the health parameters of a gas turbine, a piecewise linear model is developed using the proposed approach, and this model is verified in a simulation environment. The results show that the developed piecewise linear model can capture the behavior of a gas turbine quite closely. Then, a linearized Kalman filter is designed for estimating the health parameters under steady-state and transient conditions. The results show that the generalized linear model established using the presented method can be used to accurately estimate the health parameters of a gas turbine.
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McCain, B. A., and A. G. Stefanopoulou. "Order Reduction for a Control-Oriented Model of the Water Dynamics in Fuel Cells." In ASME 2006 4th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2006. http://dx.doi.org/10.1115/fuelcell2006-97075.

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Predicting the water dynamics and estimating humidity and flooding conditions in a low-temperature fuel cell are critical for robust operation and long life. Previous work by McKay et al [1] shows that the fuel cell anode, cathode, and membrane water dynamics and gaseous species concentrations can be accurately modeled by discretizing the partial differential equations that describe mass transport into three segments. Avoiding sensitivities associated with over-parameterization, and allowing for the real-time computations necessary for embedded controllers, requires in-depth investigation of the model order. In this paper the model from [1] is formulated into a bond graph representation. The objective is to establish the necessary model order for the fuel cell model using the Model Order Reduction Algorithm (MORA) [2], where an energy-based metric termed the Activity is used to quantify the contribution of each element of the model. Activity is a scalar quantity that is determined from the generalized effort and flow through each element of the model. We show the degree of model order reduction and provide a guideline for appropriate discretization.
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Li, Chen, and Liu Yanzhu. "The Robust Adaptive Control of Free-Floating Space Manipulator Systems." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21534.

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Abstract In this paper, the kinematics and dynamics of free-floating space manipulator systems are analyzed, and it is shown that the Jacobian matrix and the dynamic equations of the system are nonlinearly dependent on inertial parameters. In order to overcome the above problems, the system is modeled as under-actuated robot system, and the idea of augmentation approach is adopted. It is demonstrate that the augmented generalized Jacobian matrix and the dynamic equations of the system can be linearly dependent on a group of inertial parameters. Based on the results, the robust adaptive control scheme for free-floating space manipulator with uncertain inertial parameters to track the desired trajectory in workspace is proposed, and a two-link planar space manipulator system is simulated to verify the proposed control scheme. The proposed control scheme is computationally simple, because we choose to make the controller robust to the uncertain inertial parameters rather than explicitly estimating them online. In particular, it require neither measuring the position, velocity and acceleration of the floating base with respect to the orbit nor controlling the position and attitude angle of the floating base.
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Gavrea, B., D. Negrut, and F. A. Potra. "The Newmark Integration Method for Simulation of Multibody Systems: Analytical Considerations." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81770.

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When simulating the behavior of a mechanical system, the time evolution of the generalized coordinates used to represent the configuration of the model is computed as the solution of a combined set of ordinary differential and algebraic equations (DAEs). There are several ways in which the numerical solution of the resulting index 3 DAE problem can be approached. The most well-known and time-honored algorithms are the direct discretization approach, and the state-space reduction approach, respectively. In the latter, the problem is reduced to a minimal set of potentially new generalized coordinates in which the problem assumes the form of a pure second order set of Ordinary Differential Equations (ODE). This approach is very accurate, but computationally intensive, especially when dealing with large mechanical systems that contain flexible parts, stiff components, and contact/impact. The direct discretization approach is less but nevertheless sufficiently accurate yet significantly faster, and it is the approach that is considered in this paper. In the context of direct discretization methods, approaches based on the Backward Differentiation Formulas (BDF) have been the traditional choice for more than 20 years. This paper proposes a new approach in which BDF methods are replaced by the Newmark formulas. Local convergence analysis is carried out for the proposed method, and step-size control, error estimation, and nonlinear system solution related issues are discussed in detail. A series of two simple models are used to validate the method. The global convergence analysis and a computational-efficiency comparison with the most widely used numerical integrator available in the MSC.ADAMS commercial simulation package are forthcoming. The new method has been implemented successfully for industrial strength Dynamic Analysis simulations in the 2005 version of the MSC.ADAMS software and used very effectively for the simulation of systems with more than 15,000 differential-algebraic equations.
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Milliet de Faverges, Marie, Giorgio Russolillo, Christophe Picouleau, Boubekeur Merabet, and Bertrand Houzel. "Estimating Long-Term Delay Risk with Generalized Linear Models." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569507.

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D'Angelo, G. M., N. A. Lazar, W. F. Eddy, J. C. Morris, and Y. I. Sheline. "A generalized estimating equations approach for resting-state functional MRI group analysis." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091254.

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Beghi, A., and D. D'Alessandro. "Some remarks on FSN models and generalized Riccati equations." In 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082502.

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Zha, Li-teng, Zhi-bin Li, Xiang Zhang, and Liu-yi Gao. "Using Generalized Estimating Equation Model to Analyze Crash Frequency on Freeways in China." In 11th International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41186(421)228.

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Karam, Nada S., and Ahmed H. Khaleel. "Generalized inverse Rayleigh reliability estimation for the (2+1) cascade model." In XIAMEN-CUSTIPEN WORKSHOP ON THE EQUATION OF STATE OF DENSE NEUTRON-RICH MATTER IN THE ERA OF GRAVITATIONAL WAVE ASTRONOMY. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5116973.

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Reports on the topic "Models of generalized estimating equations"

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Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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Lubowa, Nasser, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Pharmaceutical Industry in Uganda: A Review of the Common GMP Non-conformances during Regulatory Inspections. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317442.

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The prevalence of substandard medicines in Africa is high but not well documented. Low and Middle-Income Countries (LMICs) are likely to face considerable challenges with substandard medications. Africa faces inadequate drug regulatory practices, and in general, compliance with Good Manufacturing Practices (GMP) in most of the pharmaceutical industries is lacking. The majority of pharmaceutical manufacturers in developing countries are often overwhelmed by the GMP requirements and therefore are unable to operate in line with internationally acceptable standards. Non-conformances observed during regulatory inspections provide the status of the compliance to GMP requirements. The study aimed to identify the GMP non-conformances during regulatory inspections and gaps in the production of pharmaceuticals locally manufactured in Uganda by review of the available 50 GMP reports of 21 local pharmaceutical companies in Uganda from 2016. The binary logistic generalized estimating equations (GEE) model was applied to estimate the association between odds of a company failing to comply with the GMP requirements and non-conformances under each GMP inspection parameter. Analysis using dummy estimation to linear regression included determination of the relationship that existed between the selected variables (GMP inspection parameters) and the production capacity of the local pharmaceutical industry. Oral liquids, external liquid preparations, powders, creams, and ointments were the main categories of products manufactured locally. The results indicated that 86% of the non-conformances were major, 11% were minor, and 3% critical. The majority of the non-conformances were related to production (30.1%), documentation (24.5%), and quality control (17.6%). Regression results indicated that for every non-conformance under premises, equipment, and utilities, there was a 7-fold likelihood of the manufacturer failing to comply with the GMP standards (aOR=6.81, P=0.001). The results showed that major non-conformances were significantly higher in industries of small scale (B=6.77, P=0.02) and medium scale (B=8.40, P=0.04), as compared to those of large scale. This study highlights the failures in quality assurance systems and stagnated GMP improvements in these industries that need to be addressed by the manufacturers with support from the regulator. The addition of risk assessment to critical production and quality control operations and establishment of appropriate corrective and preventive actions as part of quality management systems are required to ensure that quality pharmaceuticals are manufactured locally.
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Cerulli, Giovanni. Estimating Dose-Response Functions in Stata. Instats Inc., 2023. http://dx.doi.org/10.61700/iiawi76rkf2fr469.

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This seminar equips researchers with the knowledge and skills to identify and estimate dose-response functions using Stata, offering an in-depth exploration of two prominent approaches for estimating dose-response functions: the Generalized Propensity Score (GPS) method and the Regression-adjustment-based Dose-Response Models (CTREAT). These methods are widely used in the field of causal inference with continuous treatment (or exposure) and have applications in various domains such as medicine, social sciences, and economics. The seminar includes practical exercises, a Q&A session, and post-seminar support, offering participants a deep understanding of dose-response functions and their application in various research fields. An official Instats certificate of completion is provided along with 1 ECTS equivalent points.
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Budzich, Jeffrey. PR-685-184506-R05 Fluvial Geomorphology Equations and Mechanics. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2020. http://dx.doi.org/10.55274/r0011666.

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Channel hydrology, hydraulics, and sediment composition are key variables to calculating vertical and horizontal channel movement. A variety of methods are available for estimating channel bed scour, bank erosion, and channel migration with fewer available to predict avulsion potential. These methods vary in complexity from simplified empirical and theoretical equations to complex multi-dimensional models that may be used to understand potential hydrotechnical threats to pipelines and other structures. Furthermore, there are a variety of publicly available resources of relevant information to enhance pipeline operators' development and implementation of an effective water crossing program. The public resources include the United States Geological Survey, the National Weather Service within the National Oceanic and Atmospheric Administration, Federal Emergency Management Administration, United States Department of Agriculture, Natural Resource Conservation Service, and the Government of Canada.
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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors.
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Moreda, Fekadu, Benjamin Lord, Mauro Nalesso, Pedro Coli Valdes Daussa, and Juliana Corrales. Hydro-BID: New Functionalities (Reservoir, Sediment and Groundwater Simulation Modules). Inter-American Development Bank, November 2016. http://dx.doi.org/10.18235/0009312.

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The Inter-American Development Bank (IDB) provides financial and technicalsupport for infrastructure projects in water and sanitation, irrigation, flood control, transport, and energy, and for development projects in agriculture, urban systems, and natural resources. Many of these projects depend upon water resources and may be affected negatively by climate change and other developments that alter water availability, such as population growth and shifts in land use associated with urbanization, industrial growth, and agricultural practices. Assessing the potential for future changes in water availability is an important step toward ensuring that infrastructure and other development projects meet their operational, financial, and economic goals. It is also important to examine the implications of such projects for the future allocation of available water among competing users and uses to mitigate potential conflict and to ensure such projects are consistent with long-term regional development plans and preservation of essential ecosystem services. As part of its commitment to help member countries adapt to climate change, the IDB is sponsoring work to develop and apply the Regional Water Resources Simulation Model for Latin America and the Caribbean, an integrated suite of watershed modeling tools known as Hydro-BID. Hydro-BID is a highly scalable modeling system that includes hydrology and climate analysis modules to estimate the availability of surface water (stream flows) at the regional, basin, and sub-basin scales. The system includes modules for incorporating the effects of groundwater and reservoirs on surface water flows and for estimating sediment loading. Data produced by Hydro-BID are useful for water balance analysis, water allocation decisions, and economic analysis and decision support tools to help decision-makers make informed choices among alternative designs for infrastructure projects and alternative policies for water resources management. IDB sponsored the development of Hydro-BID and provides the software and basic training free of charge to authorized users; see hydrobidlac.org. The system was developed by RTI International as an adaptation of RTI's proprietary WaterFALL® modeling software, based on over 30 years of experience developing and using the U.S. National Hydrography Dataset (NHDPlus) in support to the U.S. Geological Survey and the U.S. Environmental Protection Agency. In Phase I of this effort, RTI prepared a working version of Hydro-BID that includes: (1) the Analytical Hydrography Dataset for Latin America and the Caribbean (LAC AHD), a digital representation of 229,300 catchments in Central America, South America, and the Caribbean with their corresponding topography, river, and stream segments; (2) a geographic information system (GIS)-based navigation tool to browse AHD catchments and streams with the capability of navigating upstream and downstream; (3) a user interface for specifying the area and period to be modeled and the period and location for which water availability will be simulated; (4) a climate data interface to obtain rainfall and temperature inputs for the area and period of interest; (5) a rainfall-runoff model based on the Generalized Watershed Loading Factor (GWLF) formulation; and (6) a routing scheme for quantifying time of travel and cumulative flow estimates across downstream catchments. Hydro-BID generates output in the form of daily time series of flow estimates for the selected location and period. The output can be summarized as a monthly time series at the user's discretion. In Phase II of this effort, RTI has prepared an updated version of Hydro-BID that includes (1) improvements to the user interface; (2) a module to simulate the effect of reservoirs on downstream flows; (3) a module to link Hydro-BID and groundwater models developed with MODFLOW and incorporate water exchanges between groundwater and surface water compartments into the simulation of sur
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