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Relevant bibliographies by topics / Generalized Estimating Equation approach (GEE)

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Academic literature on the topic 'Generalized Estimating Equation approach (GEE)'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Generalized Estimating Equation approach (GEE)"

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Shults, Justine, Sarah J. Ratcliffe, and Mary Leonard. "Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (2007): 147–66. http://dx.doi.org/10.1177/1536867x0700700201.

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Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (gee) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of qls that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of qls: (1) qls allows some correlation structures that have not yet been implemented in the framework of gee, (2) qls can be applied as an alternative to gee if the gee est

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Hidayati, Rizka Dwi, I. Made Tirta, and Yuliani Setia Dewi. "The Efficiency of First (GEE1) and Second (GEE2) Order “Generalized Estimating Equations” for Longitudinal Data." Jurnal ILMU DASAR 15, no. 1 (2014): 29. http://dx.doi.org/10.19184/jid.v15i1.553.

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The approach of GEE focuses on a linear model for the mean of the observations in the cluster without full specification the distribution of full-on observation. GEE is a marginal model where is not based on the full likelihood of the response, but only based on the relationship between the mean (first moment) and variance (second moment) as well as the correlation matrix. The advantage of GEE is that the mean of parameter are estimated consistently regardless whether the correlation structure is specified correctly or not, as long as the mean has the correct specifications. However, the effic

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Chaganty, N. Rao, Roy Sabo, and Yihao Deng. "Alternatives to Mixture Model Analysis of Correlated Binomial Data." ISRN Probability and Statistics 2012 (May 28, 2012): 1–10. http://dx.doi.org/10.5402/2012/896082.

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While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlati

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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 (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 (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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Lange, Christoph, and John C. Whittaker. "Mapping Quantitative Trait Loci Using Generalized Estimating Equations." Genetics 159, no. 3 (2001): 1325–37. http://dx.doi.org/10.1093/genetics/159.3.1325.

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AbstractA number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.

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Ghisletta, Paolo, and Dario Spini. "An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals." Journal of Educational and Behavioral Statistics 29, no. 4 (2004): 421–37. http://dx.doi.org/10.3102/10769986029004421.

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Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Sw

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Westgate, Philip M. "A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE–type marginal model for binary outcomes." Clinical Trials 16, no. 1 (2018): 41–51. http://dx.doi.org/10.1177/1740774518803635.

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Background/aims Cluster randomized trials are popular in health-related research due to the need or desire to randomize clusters of subjects to different trial arms as opposed to randomizing each subject individually. As outcomes from subjects within the same cluster tend to be more alike than outcomes from subjects within other clusters, an exchangeable correlation arises that is measured via the intra-cluster correlation coefficient. Intra-cluster correlation coefficient estimation is especially important due to the increasing awareness of the need to publish such values from studies in orde

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Akanda, Md Abdus Salam, and Russell Alpizar-Jara. "A Generalized Estimating Equations Approach to Model Heterogeneity and Time Dependence in Capture-Recapture Studies." European Journal of Ecology 3, no. 1 (2017): 9–17. http://dx.doi.org/10.1515/eje-2017-0002.

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AbstractIndividual heterogeneity in capture probabilities and time dependence are fundamentally important for estimating the closed animal population parameters in capture-recapture studies. A generalized estimating equations (GEE) approach accounts for linear correlation among capture-recapture occasions, and individual heterogeneity in capture probabilities in a closed population capture-recapture individual heterogeneity and time variation model. The estimated capture probabilities are used to estimate animal population parameters. Two real data sets are used for illustrative purposes. A si

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Turner, Elizabeth L., Lanqiu Yao, Fan Li, and Melanie Prague. "Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness." Statistical Methods in Medical Research 29, no. 5 (2019): 1338–53. http://dx.doi.org/10.1177/0962280219859915.

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The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE v

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Dissertations / Theses on the topic "Generalized Estimating Equation approach (GEE)"

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Lyth, Johan. "En jämförelse mellan individers självuppskattade livskvalitet och samhällets hälsopreferenser : En paneldatastudie av hjärtpatienter." Thesis, Linköpings universitet, Matematiska institutionen, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15095.

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Objective: In recent years there has been an increasing interest within the clinical (medical) science in measuring people’s health. When estimating quality of life, present practise is to use the EQ-5D questionnaire and an index which weighs the different questions. The question is what happens if the individuals estimate there own health, would it differ from the public preferences? The aim is to make a new prediction model based on the opinion of patients and compare it to the present model based on public preferences. Method: A sample of 362 patients with unstable coronary artery disease f

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Sagara, Issaka. "Méthodes d'analyse statistique pour données répétées dans les essais cliniques : intérêts et applications au paludisme." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5081/document.

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De nombreuses études cliniques ou interventions de lutte ont été faites ou sont en cours en Afrique pour la lutte contre le fléau du paludisme. En zone d'endémie, le paludisme est une maladie récurrente. La revue de littérature indique une application limitée des outils statistiques appropriés existants pour l'analyse des données récurrentes de paludisme. Nous avons mis en oeuvre des méthodes statistiques appropriées pour l'analyse des données répétées d'essais thérapeutiques de paludisme. Nous avons également étudié les mesures répétées d'hémoglobine lors du suivi de traitements antipaludique

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Purnomo, Jerry Dwi Trijoyo, and 溥杰瑞. "A Modified Generalized Estimating Equation (GEE) Approach for Latent Class Models with Covariate Effects on Measured and Underlying Variables." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/t79rdw.

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博士<br>國立交通大學<br>統計學研究所<br>106<br>Recently, the regression extension of latent class analysis (RLCA) models have played an important role in many fields of research. RLCA models establish the relationship between primary covariates and latent class membership as well as the mediated direct effect of secondary covariates on measured responses. They have proven helpful for analyzing the relationship between measured multiple responses and covariates of interest. In this paper, we propose a generalized estimating equation (GEE) approach for the parameter estimation of RLCA models. This approach a

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Hasan, Md Tariqul. "Analyzing longitudinally correlated failure time data : a generalized estimating equation approach /." 2001.

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Song, Xiaoyu. "A New Estimating Equation Based Approach for Secondary Trait Analyses in Genetic Case-control Studies." Thesis, 2015. https://doi.org/10.7916/D8T15DB5.

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Background/Aims: Case-control designs are commonly employed in genetic association studies. In addition to the primary trait of interest, data on additional secondary traits, related to the primary trait, are often collected. Traditional association analyses between genetic variants and secondary traits can be biased in such cases, and several methods have been proposed to address this issue, including the inverse-probability-of-sampling-weighted (IPW) approach and semi-parametric maximum likelihood (SPML) approach. Methods: Here, we propose a set of new estimating equation based approach tha

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Conference papers on the topic "Generalized Estimating Equation approach (GEE)"

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Awalluddin, Asep S., Inge Wahyuni, and Hilda Nurmuslimah. "Analysis of Longitudinal Regression Model Using the Generalized Estimating Equation (GEE) for the Child Welfare Composite Index (CWCI) in West Java." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.094.

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Galski, Roberto Luiz, Heitor Patire Ju´nior, Fabiano Luis de Sousa, Jose´ Nivaldo Hinckel, Pedro Lacava, and Fernando Manuel Ramos. "GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47584.

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In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known in

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Abdullayev, P. S. "Fuzzy Statistics Approach for Aviation GTE Condition Estimation Technique." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95791.

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In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basi

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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

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