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Academic literature on the topic 'Generalised Estimating Equation'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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Journal articles on the topic "Generalised Estimating Equation"

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Zhang, Fanchang, Jingyang Yang, Chuanhui Li, Dong Li, and Yang Gao. "Direct inversion for reservoir parameters from prestack seismic data." Journal of Geophysics and Engineering 17, no. 6 (October 24, 2020): 993–1004. http://dx.doi.org/10.1093/jge/gxaa058.

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Abstract Reliably estimating reservoir parameters is the final target in reservoir characterisation. Conventionally, estimating reservoir characters from seismic inversion is implemented by indirect approaches. The indirect estimation of reservoir parameters from inverted elastic parameters, however, will produce large bias due to the propagation of errors in the procedure of inversion. Therefore, directly obtaining reservoir parameters from prestack seismic data through a rock-physical model and prestack amplitude variation with offset (AVO) inversion is proposed. A generalised AVO equation in terms of oil-porosity (OP), sand indicator (SI) and density is derived by combining a physical rock model and the Aki–Richards equation in a whole system. This makes it possible to perform direct inversion for reservoir parameters. Next, under Bayesian theorem, we develop a robust prestack inversion approach based on the new AVO equation. Tests on synthetic seismic gathers show that it can dramatically reduce the prediction error of reservoir parameters. Furthermore, field data application illustrates that reliable reservoir parameters can be directly obtained from prestack inversion.

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Curtin, François. "Meta-analysis combining parallel and crossover trials using generalised estimating equation method." Research Synthesis Methods 8, no. 3 (June 5, 2017): 312–20. http://dx.doi.org/10.1002/jrsm.1242.

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Purnomo, Jerry Dwi Trijoyo, Chih-Rung Chen, and Guan-Hua Huang. "A Modified Model-Selection Criteria in a Generalised Estimating Equation for Latent Class Regression Models." MATEMATIKA 35, no. 2 (July 31, 2019): 201–11. http://dx.doi.org/10.11113/matematika.v35.n2.1175.

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In recent years, generalised estimating equations (GEEs) have played an important role in many fields of research, such as biomedicine. In this paper, we use GEEs for latent class regression (LCR) with covariate effects on underlying and measured variables. However, there are only a few model-selection criteria in GEEs. The widely known Akaike information criterion (AIC) cannot be used directly, since AIC is a full likelihood-based model, whereas GEEs are nonlikelihood based. Hence, we propose a modification to AIC in GEEs for (LCR) models, where the likelihood is replaced by quasi-likelihood, and a proper adjustment is made by giving a penalty term. The data of the modified hospital elder life program (mHELP) project are used to illustrate our method.

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SPEYBROECK, N., F. BOELAERT, D. RENARD, T. BURZYKOWSKI, K. MINTIENS, G. MOLENBERGHS, and D. L. BERKVENS. "Design-based analysis of surveys: a bovine herpesvirus 1 case study." Epidemiology and Infection 131, no. 2 (October 2003): 991–1002. http://dx.doi.org/10.1017/s095026880300102x.

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This paper critically assesses the design implications for the analysis of surveys of infections. It indicates the danger of not accounting for the study design in the statistical investigation of risk factors. A stratified design often implies an increased precision while clustering of infection results in a decreased precision. Through pseudo-likelihood estimation and linearisation of the variance estimator, the design effects can be taken into account in the analysis. The intra-cluster-correlation can be investigated through a logistic random effect model and a generalised estimating equation (GEE), allowing the investigation of the extent of spread of infections in a herd (cluster). The advantage of using adaptive Gaussian quadrature in a logistic random effect model is discussed. Applicable software is briefly reviewed. The methods are illustrated with data from a bovine herpesvirus 1 (BHV-1) serosurvey of Belgian cattle.

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Hurst, Yumi, and Haruhisa Fukuda. "Effects of changes in eating speed on obesity in patients with diabetes: a secondary analysis of longitudinal health check-up data." BMJ Open 8, no. 1 (January 2018): e019589. http://dx.doi.org/10.1136/bmjopen-2017-019589.

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ObjectiveFew studies have examined the causal relationships between lifestyle habits and obesity. With a focus on eating speed in patients with type 2 diabetes, this study aimed to analyse the effects of changes in lifestyle habits on changes in obesity using panel data.MethodsPatient-level panel data from 2008 to 2013 were generated using commercially available insurance claims data and health check-up data. The study subjects comprised Japanese men and women (n=59 717) enrolled in health insurance societies who had been diagnosed with type 2 diabetes during the study period. Body mass index (BMI) was measured, and obesity was defined as a BMI of 25 or more. Information on lifestyle habits were obtained from the subjects’ responses to questions asked during health check-ups. The main exposure of interest was eating speed (‘fast’, ‘normal’ and ‘slow’). Other lifestyle habits included eating dinner within 2 hours of sleeping, after-dinner snacking, skipping breakfast, alcohol consumption frequency, sleep adequacy and tobacco consumption. A generalised estimating equation model was used to examine the effects of these habits on obesity. In addition, fixed-effects models were used to assess these effects on BMI and waist circumference.ResultsThe generalised estimating equation model showed that eating slower inhibited the development of obesity. The ORs for slow (0.58) and normal-speed eaters (0.71) indicated that these groups were less likely to be obese than fast eaters (P<0.001). Similarly, the fixed-effects models showed that eating slower reduced BMI and waist circumference. Relative to fast eaters, the coefficients of the BMI model for slow and normal-speed eaters were −0.11 and −0.07, respectively (P<0.001).DiscussionChanges in eating speed can affect changes in obesity, BMI and waist circumference. Interventions aimed at reducing eating speed may be effective in preventing obesity and lowering the associated health risks.

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Gibb, Sheree J., David M. Fergusson, and L. John Horwood. "Relationship duration and mental health outcomes: findings from a 30-year longitudinal study." British Journal of Psychiatry 198, no. 1 (January 2011): 24–30. http://dx.doi.org/10.1192/bjp.bp.110.083550.

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BackgroundMarriage is known to be associated with improved mental health, but little research has examined whether the duration of a cohabiting relationship is associated with mental health.AimsTo examine the associations between relationship duration and mental health problems in a birth cohort of 30-year-olds.MethodAssociations between relationship duration and mental health were examined using a generalised estimating equation approach. Associations were adjusted for covariates, including prior mental health problems.ResultsLonger relationship duration was significantly associated with lower rates of depression, suicidal behaviour and substance abuse/dependence, even after adjustment for covariates. In most cases the associations did not vary with gender. Legal relationship status (legally or de facto married) was not significantly related to mental health once due allowance was made for relationship duration.ConclusionsIncreasing relationship duration, but not legal relationship status, has a protective effect on mental health for men and women.

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Campa, Francesco, Catarina N. Matias, Tatiana Moro, Giuseppe Cerullo, Andrea Casolo, Filipe J. Teixeira, and Antonio Paoli. "Methods over Materials: The Need for Sport-Specific Equations to Accurately Predict Fat Mass Using Bioimpedance Analysis or Anthropometry." Nutrients 15, no. 2 (January 5, 2023): 278. http://dx.doi.org/10.3390/nu15020278.

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Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.

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Chih, An-Hsuan, Yang-Ching Chen, Yu-Kang Tu, Kuo-Chin Huang, Tai-Yuan Chiu, and Yungling Leo Lee. "Mediating pathways from central obesity to childhood asthma: a population-based longitudinal study." European Respiratory Journal 48, no. 3 (May 12, 2016): 748–57. http://dx.doi.org/10.1183/13993003.00226-2016.

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The mediating pathways linking obesity and asthma are largely unknown. We aimed to investigate the mediating pathways and to search for the most prominent pathological mechanism between central obesity and childhood asthma.In the Taiwan Children Health Study, we collected data on an open cohort of children aged 9–13 years. Children's respiratory outcomes, atopic conditions, obesity measures and pulmonary function were surveyed annually between 2010 and 2012. Exhaled nitric oxide fraction concentrations were recorded in 2012. Generalised estimating equations and general linear models were used to examine the associations between central obesity, possible mediators and asthma. Structural equation models were applied to investigate the pathways that mediate the link between central obesity and asthma.Central obesity (waist-to-hip ratio) most accurately predicted childhood asthma. In the active asthma model, the percentage of mediation was 28.6% for pulmonary function, 18.1% for atopy and 5.7% for airway inflammation. The percentage of mediation for pulmonary function was 40.2% in the lifetime wheeze model. Pulmonary function was responsible for the greatest percentage of mediation among the three mediators in both models.Decline in pulmonary function is the most important pathway in central obesity related asthma. Pulmonary function screening should be applied to obese children for asthma risk prediction.

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Dragioti, Elena, Björn Gerdle, and Britt Larsson. "Longitudinal Associations between Anatomical Regions of Pain and Work Conditions: A Study from The SwePain Cohort." International Journal of Environmental Research and Public Health 16, no. 12 (June 19, 2019): 2167. http://dx.doi.org/10.3390/ijerph16122167.

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We investigated the time-based associations between workload (physical and mechanical), psychosocial work stressors (demands, control, and support), and the number of anatomical regions with pain (ARP). This population-based study with a two-year follow-up included 11,386 responders (5125 men, 6261 women; mean age: 48.8 years; SD: 18.5) living in south-eastern Sweden. Predictive associations were assessed through generalised linear models, and changes over time were examined using a generalised estimating equation. The results of both models were reported as parameter estimates (B) with 95% confidence interval (CIs). Mean changes in the number of ARP, workload, and psychosocial work stressors were stable over time. High mechanical workload and job demands were likely associated with the number of ARP at the two-year follow-up. In the reverse prospective model, we found that the number of ARP was also associated with high physical and mechanical workload and low job control and support. In the two time-based models of changes, we found a reciprocal association between number of ARP and mechanical workload. Our results add epidemiological evidence to the associations between work conditions and the extent of pain on the body. Components of work conditions, including job demands and mechanical strain, must be considered when organisations and health policy makers plan and employ ergonomic evaluations to minimise workplace hazards in the general population.

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Lukas, M. A. "Assessing regularised solutions." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 30, no. 1 (July 1988): 24–42. http://dx.doi.org/10.1017/s0334270000006019.

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AbstractConsider the prototype ill-posed problem of a first kind integral equation ℛ with discrete noisy data di, = f(xi) + εi, i = 1, …, n. Let u0 be the true solution and unα a regularised solution with regularisation parameter α. Under certain assumptions, it is known that if α → 0 but not too quickly as n → ∞, then unα converges to u0. We examine the dependence of the optimal sequence of α and resulting optimal convergence rate on the smoothness of f or u0, the kernel K, the order of regularisation m and the error norm used. Some important implications are made, including the fact that m must be sufficiently high relative to the smoothness of u0 in order to ensure optimal convergence. An optimal filtering criterion is used to determine the order where is the maximum smoothness of u0. Two practical methods for estimating the optimal α, the unbiased risk estimate and generalised cross validation, are also discussed.

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Dissertations / Theses on the topic "Generalised Estimating Equation"

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Lange, Christoph. "Generalized estimating equation methods in statistical genetics." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269921.

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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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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.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy

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Zhang, Xiaohong. "Generalized estimating equations for clustered survival data." [Ames, Iowa : Iowa State University], 2006.

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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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Sepato, Sandra Moepeng. "Generalized linear mixed model and generalized estimating equation for binary longitudinal data." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/43143.

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The most common analysis used for binary data is generalised linear model (GLM) with either a binomial or bernoulli distribution using either a logit, probit, complementary log-log or other type of link functions. However, such analyses violate the independence assumption if the binary data are measured repeatedly over time at the same subject or site. Failure to take into account the correlation can lead to incorrect estimation of regression parameters and the estimates are less efficient, particularly when the correlations are large. Therefore, to obtain the most efficient estimates that are also unbiased the methods that incorporate correlations (McCullagh and Nelder, 1989) should be used. Two of the statistical methodologies that can be used to account for this correlation for the longitudinal data are the generalized linear mixed models (GLMMs) and generalized estimating equation (GEE). The GLMM method is based on extending the fixed effects GLM to include random effects and covariance patterns. Unlike the GLM and GLMM methods, the GEE method is based on the quasi-likelihood theory and no assumption is made about the distribution of response observations (Liang and Zeger, 1986). The main objective of the study is to investigate the statistical properties and limitations of these three approaches, i.e. GLM, GLMMs and GEE for analyzing longitudinal data through use of a binary data from an entomology study. The results reaffirms the point made by these authors that misspecification of working correlation in GEE approach would still give consistent regression parameter estimates. Further, the results of this study suggest that even with small correlation, ignoring a random effects in a binary model can lead to inconsistent estimation.
Dissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
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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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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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Zhao, Chen. "Evaluating Health Policy Effect with Generalized Linear Model and Generalized Estimating Equation Model." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586377218891854.

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Books on the topic "Generalised Estimating Equation"

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

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

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

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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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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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Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Chapman & Hall/CRC, 2002.

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Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2003.

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Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2002.

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Generalized Estimating Equations. CRC Press, 2012.

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Book chapters on the topic "Generalised Estimating Equation"

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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. "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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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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Ziegler, Andreas. "Pseudo maximum likelihood estimation based on the quadratic exponential family." In Generalized Estimating Equations, 101–17. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_7.

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Ziegler, Andreas. "Generalized method of moment estimation." In Generalized Estimating Equations, 119–31. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_8.

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Islam, M. Ataharul, and Rafiqul I. Chowdhury. "Generalized Estimating Equation." In Analysis of Repeated Measures Data, 161–67. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_12.

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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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Conference papers on the topic "Generalised Estimating Equation"

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Gayen, Atin, and M. Ashok Kumar. "Generalized Estimating Equation for the Student-t Distributions." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437622.

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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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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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Pham, Hoang Duc, Katja Tuting, Heyno Garbe, and Sven Fisahn. "Estimation of Measurement Uncertainties in TEM-Cells based on Generalized Telegraphist’s Equation." In 2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium. IEEE, 2021. http://dx.doi.org/10.1109/emc/si/pi/emceurope52599.2021.9559231.

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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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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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Luo, Yongchao, Jianjun Wang, Siyu Chen, and Xiaocong Cui. "A parameter estimation method for structural equation model based on generalized maximum entropy." In 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2012. http://dx.doi.org/10.1109/icicip.2012.6391494.

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Asiri, Sharefa. "Modulating functions method for coefficients estimation in the sixth order generalized Boussinesq equation." In The 5th Innovation and Analytics Conference & Exhibition (IACE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0092769.

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Channgam, Taweesak. "Performance of generalized estimating equations using bias-corrected sandwich variance estimators with count outcomes." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012232.

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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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Reports on the topic "Generalised Estimating Equation"

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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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