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Journal articles on the topic 'Generalized linear mixed model (GLMM)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Garrido, José, and Jun Zhou. "Full Credibility with Generalized Linear and Mixed Models." ASTIN Bulletin 39, no. 1 (2009): 61–80. http://dx.doi.org/10.2143/ast.39.1.2038056.

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AbstractGeneralized linear models (GLMs) are gaining popularity as a statistical analysis method for insurance data. For segmented portfolios, as in car insurance, the question of credibility arises naturally; how many observations are needed in a risk class before the GLM estimators can be considered credible? In this paper we study the limited fluctuations credibility of the GLM estimators as well as in the extended case of generalized linear mixed model (GLMMs). We show how credibility depends on the sample size, the distribution of covariates and the link function. This provides a mechanis

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Hayati, Ma'rufah, and Agus Muslim. "Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling." Enthusiastic : International Journal of Applied Statistics and Data Science 1, no. 01 (2021): 36–52. http://dx.doi.org/10.20885/enthusiastic.vol1.iss1.art6.

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Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate d

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Fox, Jean-Paul, Duco Veen, and Konrad Klotzke. "Generalized Linear Mixed Models for Randomized Responses." Methodology 15, no. 1 (2019): 1–18. http://dx.doi.org/10.1027/1614-2241/a000153.

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Abstract. Response bias (nonresponse and social desirability bias) is one of the main concerns when asking sensitive questions about behavior and attitudes. Self-reports on sensitive issues as in health research (e.g., drug and alcohol abuse), and social and behavioral sciences (e.g., attitudes against refugees, academic cheating) can be expected to be subject to considerable misreporting. To diminish misreporting on self-reports, indirect questioning techniques have been proposed such as the randomized response techniques. The randomized response techniques avoid a direct link between individ

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Setiawan, Erwan, Khairil Anwar Notodiputro, and Bagus Sartono. "Generalized Linear Mixed-Model Tree for Modeling Dengue Fever Cases." CogITo Smart Journal 10, no. 2 (2024): 380–92. https://doi.org/10.31154/cogito.v10i2.715.380-392.

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The GLMM tree demonstrates flexibility when applied to complex dataset structures such as multilevel and longitudinal data. However, there has been no assessment of the performance of GLMM trees on panel data structures. This study aims to assess the performance of the GLMM tree on a panel data structure using a case study of dengue fever cases in West Java. The performance evaluation focuses on the accuracy of the model. The dataset includes cross-sectional data from 27 regencies/cities in West Jawa, covering different regions at a single point in time, and time-series data from 2014 to 2022,

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Nurfadilah, Khalilah, Asfar, Khairil A. Notodiputro, Bagus Sartono, and Azlam Nas. "Premarital Sex Behavior Model with Generalized Linear Mixed Model Least Absolute Shrinkage and Selection Operator dan Generalized Linear Mixed Model GROUP Least Absolute Shrinkage and Selection Operator." STATISTIKA Journal of Theoretical Statistics and Its Applications 23, no. 1 (2023): 48–56. http://dx.doi.org/10.29313/statistika.v23i1.1953.

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ABSTRACT Premarital sexual behavior is sexual behavior that is carried out between men and women without legal marriage. As the number of premarital sex increases, efforts need to take. One that can do is to identify the main factors contributing to reducing or increasing premarital sex behavior by a Regression model. In the context of sexual behavior, environmental influences cannot be ignored. GLMM is used to model data that is grouped into certain Groups, include environment effect that is modeled as mixed effect in GLMM. In terms of parsimony, the LASSO method can do selection variab

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Zhu, Rui, Chao Jiang, Xiaofeng Wang, Shuang Wang, Hao Zheng, and Haixu Tang. "Privacy-preserving construction of generalized linear mixed model for biomedical computation." Bioinformatics 36, Supplement_1 (2020): i128—i135. http://dx.doi.org/10.1093/bioinformatics/btaa478.

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Abstract Motivation The generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by th

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MAIORANO, Amanda Marchi, Thiago Santos MOTA, Ana Carolina VERDUGO, et al. "COMPARATIVE STUDY OF CATTLE TICK RESISTANCE USING GENERALIZED LINEAR MIXED MODELS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 1 (2019): 41. http://dx.doi.org/10.28951/rbb.v37i1.341.

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Comparison of tick resistance in Bos taurus indicus (Nelore) and Bos taurus taurus (Simmental and Caracu) subspecies was investigated utilizing generalized linear mixed models (GLMMs) with Poisson and Negative binomial distributions. Nelore animals (NE) are known to present greater resistance than t. taurus. Difference between tick resistance in Simmental (SI) and Caracu (CA) breeds has never been reported previously. Three artificial tick infestations were conducted to evaluate tick resistance in these breeds. The statistic point of the present study was to show alternative models for the eva

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Jun, Sunghae. "Text Data Analysis Using Generalized Linear Mixed Model and Bayesian Visualization." Axioms 11, no. 12 (2022): 674. http://dx.doi.org/10.3390/axioms11120674.

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Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms have been studied for text data analysis. Most of them were analytical methods based on the generalized linear model (GLM). For the GLM, text data analysis is performed based on the assumption of the error included in the given data and follows the Gaussian distribution. However, the GLM has shown limitations in the analysis of text data, including

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Dietz, L. R., and S. Chatterjee. "Logit-normal mixed model for Indian monsoon precipitation." Nonlinear Processes in Geophysics 21, no. 5 (2014): 939–53. http://dx.doi.org/10.5194/npg-21-939-2014.

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Abstract. Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the mode

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Zhong, Yuan, Baoxin Hu, G. Brent Hall, Farah Hoque, Wei Xu, and Xin Gao. "A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion." ISPRS International Journal of Geo-Information 9, no. 7 (2020): 414. http://dx.doi.org/10.3390/ijgi9070414.

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The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ont

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Bono, Roser, Rafael Alarcón, Jaume Arnau, F. Javier García-Castro, and Maria J. Blanca. "Robustness of Generalized Linear Mixed Models for Split-Plot Designs with Binary Data." Anales de Psicología 39, no. 2 (2023): 332–43. http://dx.doi.org/10.6018/analesps.527421.

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This paper examined the robustness of the generalized linear mixed model (GLMM). The GLMM estimates fixed and random effects, and it is especially useful when the dependent variable is binary. It is also useful when the dependent variable involves repeated measures, since it can model correlation. The present study used Monte Carlo simulation to analyze the empirical Type I error rates of GLMMs in split-plot designs. The variables manipulated were sample size, group size, number of repeated measures, and correlation between repeated measures. Extreme conditions were also considered, including

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Rosos Djikpo, Vignon Adelphe, Oscar Teka, Sandrine Abalo, Elodie Hozanhekpon, Ghislaine Noudehou, and Brice Sinsin. "Quantifying Street Tree Regulating Heat Effects Using a Generalized Linear Mixed Model Approach." European Scientific Journal, ESJ 19, no. 27 (2023): 36. http://dx.doi.org/10.19044/esj.2023.v19n27p36.

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Climate change has emerged as a significant global environmental concern, prompting increased interest in utilizing trees as an alternative means to enhance human well-being and thermal comfort in urban settings. This study endeavors to assess the influence of street trees on the urban microclimate in tropical cities, employing a Generalized Linear Mixed Model (GLMM) approach. The investigation was conducted in Cotonou, Porto-Novo, and Ouidah within Benin. Data collection was conducted along thoroughfares, where a systematic inventory was performed to measure various characteristics of each st

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Parreira da Silva, Guilherme, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro Júnior, and Wagner Hugo Bonat. "Multivariate Generalized Linear Mixed Models for Count Data." Austrian Journal of Statistics 53, no. 1 (2024): 44–69. http://dx.doi.org/10.17713/ajs.v53i1.1574.

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Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TM

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Islam, Tahmidul, Md Golam Rabbani, and Wasimul Bari. "Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach." Dhaka University Journal of Science 64, no. 2 (2016): 163–67. http://dx.doi.org/10.3329/dujs.v64i2.54492.

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Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model

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Sihombing, Pardomuan Robinson. "COMPARISON OF GLM, GLMM AND GEE POISSON MATHEMATICAL MODELING PERFORMANCE (Case Study: Number of Pulmonary Tuberculosis Patients in Indonesia in 2019-2021)." Jurnal TAMBORA 6, no. 3 (2022): 102–6. http://dx.doi.org/10.36761/jt.v6i3.2081.

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This study aims to compare the performance of data modeling with Poisson regression with Generalized Linear Model (GLM), Generalized Linear Mixed Model (GLMM), and Generalized Estimating Equation (GEE) modeling. The case study used is a factor that affects the number of Pulmonary Tuberculosis cases in Indonesia with panel data. Based on the AIC criteria, the smallest BIC and RMSE GLMM models perform better than GLM and GEE. In addition, GLMM modeling also has a coefficient of determination value. The results showed that the percentage of the population smoking and the percentage of Unmet Keseh

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Dietz, L. R., and S. Chatterjee. "Logit-normal mixed model for Indian Monsoon rainfall extremes." Nonlinear Processes in Geophysics Discussions 1, no. 1 (2014): 193–233. http://dx.doi.org/10.5194/npgd-1-193-2014.

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Abstract. Describing the nature and variability of Indian monsoon rainfall extremes is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Several GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data procured from the National Climatic Data Center. The logit-normal model was applied with fixed covariates of latitude, longitude, elevation,

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Rohmaniah, Siti Alfiatur, and Novita Eka Chandra. "PERHITUNGAN PREMI ASURANSI JIWA MENGGUNAKAN GENERALIZED LINEAR MIXED MODELS." Jurnal Ilmiah Teknosains 4, no. 2 (2019): 80. http://dx.doi.org/10.26877/jitek.v4i2.3004.

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The price of life insurance premiums for each person depends on the probability of death, not only based on age and gender as offered by an Indonesian insurance company. The purpose of this study is to determine premium prices on underwriting factors and frailty factors using Generalized Linear Mixed Models (GLMM). GLMM is used for modeling a combination of fixed effect heterogeneity (underwriting factors) and random effects (frailty factors) between individuals. The data used longitudinal data about underwriting factors that have Binomial distribution are taken from the Health and Retirement

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Koper, Nicola, and Micheline Manseau. "A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models." Rangifer 32, no. 2 (2012): 195. http://dx.doi.org/10.7557/2.32.2.2269.

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Resource selection functions (RSF) are often developed using satellite (ARGOS) or Global Positioning System (GPS) telemetry datasets, which provide a large amount of highly correlated data. We discuss and compare the use of generalized linear mixed-effects models (GLMM) and generalized estimating equations (GEE) for using this type of data to develop RSFs. GLMMs directly model differences among caribou, while GEEs depend on an adjustment of the standard error to compensate for correlation of data points within individuals. Empirical standard errors, rather than model-based standard errors, mus

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Isnanda, Eriski, Khairil Anwar Notodiputro, and Kusman Sadik. "Analyzing Household Expenditures with Generalized Random Forests." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 166–79. https://doi.org/10.18860/cauchy.v10i1.30104.

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This study investigates the performance of Generalized Random Forest (GRF), which has been known to be useful in understanding heterogeneous treatment effects (HTE) and non-linear relationships in high-dimensional data. In this paper the performance of GRF was compared with Random Forest (RF), Generalized Linear Mixed Model (GLMM) as continuation of previous study conducted by Athey (2019). The data utilized in this study is from the National Socioeconomic Survey (SUSENAS) to predict household per capita expenditure in West Java, Indonesia. The models are evaluated based on their ability to ha

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Sihombing, Pardomuan Robinson, Erfiani, Khairil Anwar Notodiputro, and Anang Kurnia. "Comparative Performance of GLMM and GEE for Longitudinal Beta Regression in Economic Inequality Modelling." Advance Sustainable Science Engineering and Technology 7, no. 3 (2025): 02503011. https://doi.org/10.26877/7y0xxb39.

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Due to the shortcomings of conventional Gaussian methods, specialized models are frequently needed for longitudinal data analysis with bounded outcomes, such as the Gini ratio. In order to model economic inequality in Indonesia, this study compares the effectiveness of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for beta-distributed longitudinal data. Root Mean Square Error (RMSE) and pseudo R-squared values are used to assess model performance using panel data from 10 provinces between 2018 and 2024 as well as important socioeconomic indicators. With lowe

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Fortin, Mathieu. "Population-averaged predictions with generalized linear mixed-effects models in forestry: an estimator based on Gauss−Hermite quadrature." Canadian Journal of Forest Research 43, no. 2 (2013): 129–38. http://dx.doi.org/10.1139/cjfr-2012-0268.

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Data in forestry are often spatially and (or) serially correlated. In the last two decades, mixed models have become increasingly popular for the analysis of such data because they can relax the assumption of independent observations. However, when the relationship between the response variable and the covariates is nonlinear, as is the case in generalized linear mixed models (GLMMs), population-averaged predictions cannot be obtained from the fixed effects alone. This study proposes an estimator, which is based on a five-point Gauss−Hermite quadrature, for population-averaged predictions in t

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Tovissodé, Chénangnon Frédéric, Aliou Diop, and Romain Glèlè Kakaï. "Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm." PLOS ONE 16, no. 4 (2021): e0249604. http://dx.doi.org/10.1371/journal.pone.0249604.

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Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other

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Sushko, Gennadi G., and Anastasia S. Tkachenok. "Generalized linear mixed models (GLMM) in community ecology studies using the R statistical environment." Journal of the Belarusian State University. Ecology., no. 1 (March 27, 2025): 37–47. https://doi.org/10.46646/2521-683x/2025-1-37-47.

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Data analysis in community ecology often has certain difficulties, since standard parametric methods are inapplicable due to the fact that ecological data rarely normal distributed, there are no linear relationships between variables, there may be collinearity between explanatory variables and overdispersion in data sets. The proposed article considers an approach based on the use of regression generalized linear mixed models (GLMM), which allows analyzing data from synecological studies taking into account the above difficulties, as well as including not only quantitative but also qualitative

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Asadi, Fariba, Reza Homayounfar, Mojtaba Farjam, Yaser Mehrali, Fatemeh Masaebi, and Farid Zayeri. "Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree." Archives of Iranian Medicine 27, no. 5 (2024): 239–47. http://dx.doi.org/10.34172/aim.2024.35.

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Background: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. Methods: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained

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Yan, Zhiyu, Kori S. Zachrison, Lee H. Schwamm, Juan J. Estrada, and Rui Duan. "A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data." PLOS ONE 18, no. 1 (2023): e0280192. http://dx.doi.org/10.1371/journal.pone.0280192.

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Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation resource requirements, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data wh

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Madden, L. V., W. W. Turechek, and M. Nita. "Evaluation of Generalized Linear Mixed Models for Analyzing Disease Incidence Data Obtained in Designed Experiments." Plant Disease 86, no. 3 (2002): 316–25. http://dx.doi.org/10.1094/pdis.2002.86.3.316.

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Diseased individuals (e.g., leaves, plants) typically are clustered in nature, resulting in greater heterogeneity or variability of disease incidence than would be expected for a random pattern. To account for this variability, as well as the binary nature of disease incidence and the multiple sources of variation in designed experiments, a generalized linear mixed model (GLMM) can be used to analyze collected data. GLMMs are becoming more common in many disciplines and may be preferred over analysis of variance for non-normally distributed data. We evaluated several GLMMs for analyzing the in

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Latypova, T. R., and N. Yu Stepanova. "Application Statistical Models for Interpretation Toxicological Data." IOP Conference Series: Earth and Environmental Science 988, no. 4 (2022): 042030. http://dx.doi.org/10.1088/1755-1315/988/4/042030.

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Abstract The results of bioassay on infusoria of 75 samples of bottom sediments from 6 water bodies of the Middle Volga region were analyzed using traditional nonparametric methods and statistical models Generalized linear mixed model (GLMM) and Cumulative link mixed model (CLMM). The ambiguity of the interpretation of the results of biotesting performed by nonparametric methods is due to the fact that the toxicological data often do not correspond to the normal distribution. The use of the GLMM and CLMM models allow analyze data that do not correspond to the normal distribution and made it po

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Xie, Shengkun, and Chong Gan. "Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C-Means Clustering." Risks 11, no. 6 (2023): 99. http://dx.doi.org/10.3390/risks11060099.

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Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of insurance. In this work, spatially constrained clustering is first applied to insurance loss data to form such regions, using the forward sortation area (FSA) as a basic rating unit. The groupings of FSA by spatially constrained clustering reduce the insurance rate heterogeneity caused by smaller risk exposures. Furthermore, the generalized linear

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Rahmanda, Lalu Ramzy, Adji Achmad Rinaldo Fernandes, Solimun Solimun, Lucius Ramifidiosa, and Armando Jacquis Federal Zamelina. "PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY." MEDIA STATISTIKA 16, no. 2 (2024): 124–35. http://dx.doi.org/10.14710/medstat.16.2.124-135.

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Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the resul

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Dwinata, Alona, Khairil Anwar Notodiputro, and Bagus Sartono. "A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units." CAUCHY 7, no. 1 (2021): 13–21. http://dx.doi.org/10.18860/ca.v7i1.11575.

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Generalized linear mixed models (GLMM) combined with the L1 penalty (Least Absolute Shrinkage and Selection Operator/LASSO) is called LASSO GLMM. LASSO GLMM reduces overfitting and selects predictor variables in modeling. The aim of this study is to evaluate the model's performance for predicting Covid-19 patients with certain congenital disease that require ICU based on the results of blood tests laboratory and patient’s vital signs. This study used binary response variables, 1 if the patient was admitted to the ICU and 0 if the patient was not admitted to the ICU. The fixed effect predictor

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Merritt, Ronald. "Utilizing the Generalized Linear Mixed Model for Specification and Simulation of Transient Vibration Environments." Journal of the IEST 53, no. 2 (2010): 35–49. http://dx.doi.org/10.17764/jiet.53.2.y7291022622225x3.

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Transient vibration environments are an important consideration in qualification of aircraft store components — particularly for aircraft with internal storage bays. Generally, these transient vibration environments provide high stimulus input to a store via aerodynamic forces for up to 15 seconds on numerous occasions during training. With the recent introduction of the technique of Time Waveform Replication (TWR) to laboratory testing (MIL-STD-810G Method 525), store components can be readily tested to replications of field-measured transient vibration environments. This paper demonstrates t

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Mielenz, Norbert, Joachim Spilke, and Eberhard von Borell. "Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars." Archives Animal Breeding 57, no. 1 (2015): 1–19. http://dx.doi.org/10.5194/aab-57-26-2015.

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Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and lo

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Mielenz, Norbert, Joachim Spilke, and Eberhard von Borell. "Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars." Archives Animal Breeding 57, no. 1 (2015): 1–19. http://dx.doi.org/10.7482/0003-9438-57-026.

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Abstract. Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribut

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Terra Machado, Douglas, Otávio José Bernardes Brustolini, Yasmmin Côrtes Martins, Marco Antonio Grivet Mattoso Maia, and Ana Tereza Ribeiro de Vasconcelos. "Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion." PeerJ 11 (April 3, 2023): e15145. http://dx.doi.org/10.7717/peerj.15145.

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Background Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalize

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Qazvini, Marjan. "Survival analysis of longitudinal data: the case of English population aged 50 and over." Journal of Demographic Economics 89, no. 3 (2023): 419–63. http://dx.doi.org/10.1017/dem.2023.3.

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AbstractThis study considers data from 5 waves of the English Longitudinal Study of Ageing (ELSA). We aim to study the impact of demographic and self-rated health variables including disability and diseases on the survival of the population aged 50+. The disability variables that we consider are mobility impairment, difficulties in performing Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). One of the problems with the survey study is missing observations. This may happen due to different reasons, such as errors, nonresponse and temporary withdrawals. We add

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Masuda, Michele M., and Robert P. Stone. "Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics." ICES Journal of Marine Science 72, no. 9 (2015): 2674–83. http://dx.doi.org/10.1093/icesjms/fsv163.

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Abstract The collection of continuous data on transects is a common practice in habitat and fishery stock assessments; however, the application of standard regression models that assume independence to serially correlated data is problematic. We show that generalized linear mixed models (GLMMs), i.e. generalized linear models for longitudinal data, that are normally used for studies performed over time can also be applied to other types of clustered or serially correlated data. We apply a specific GLMM for longitudinal data, a hierarchical Bayesian logistic mixed-effects model (BLMM), to a mar

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Salvatore, Fiorella Pia, Alessia Spada, Francesca Fortunato, Demetris Vrontis, and Mariantonietta Fiore. "Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease." International Journal of Environmental Research and Public Health 18, no. 9 (2021): 4652. http://dx.doi.org/10.3390/ijerph18094652.

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The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy’s Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-re

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RUSYANA, ASEP, KHAIRIL ANWAR NOTODIPUTRO, and BAGUS SARTONO. "A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala." Jurnal Natural 21, no. 2 (2021): 72–80. http://dx.doi.org/10.24815/jn.v21i2.19325.

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Generalized Linear Mixed Model (GLMM) is a framework that has a response variable, fixed effects, and random effects. The response variable comes from an exponential family, whereas random effects have a normal distribution. Estimating parameters can be calculated using the maximum likelihood method using the Laplace approach or the Gauss-Hermite Quadrature (GHQ) approach. The purpose of this study was to identify factors that trigger student's interest to continue studying at Universitas Syiah Kuala (USK) using both techniques. The GLMM is suitable for the data because the variable response h

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Groll, Andreas, and Jasmin Abedieh. "Spain retains its title and sets a new record – generalized linear mixed models on European football championships." Journal of Quantitative Analysis in Sports 9, no. 1 (2013): 51–66. http://dx.doi.org/10.1515/jqas-2012-0046.

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AbstractNowadays many approaches that analyze and predict the results of football matches are based on bookmakers’ ratings. It is commonly accepted that the models used by the bookmakers contain a lot of expertise as the bookmakers’ profits and losses depend on the performance of their models. One objective of this article is to analyze the role of bookmakers’ odds together with many additional, potentially influental covariates with respect to a national team’s success at European football championships and especially to detect covariates, which are able to explain parts of the information co

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Sharma, Lav, Irene Oliveira, Fernando Raimundo, Laura Torres, and Guilhermina Marques. "Soil Chemical Properties Barely Perturb the Abundance of Entomopathogenic Fusarium oxysporum: A Case Study Using a Generalized Linear Mixed Model for Microbial Pathogen Occurrence Count Data." Pathogens 7, no. 4 (2018): 89. http://dx.doi.org/10.3390/pathogens7040089.

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Fusarium oxysporum exhibits insect pathogenicity—however, generalized concerns of releasing phytopathogens within agroecosystems marred its entomopathogenicity-related investigations. In a previous study, soils were sampled from Douro vineyards and adjacent hedgerows. In this study, 80 of those soils were analyzed for their chemical properties and were subsequently co-related with the abundance of entomopathogenic F. oxysporum, after insect baiting of soils with Galleria mellonella and Tenebrio molitor larvae. The soil chemical properties studied were organic matter content; total organic carb

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PUTRI, YOHANA HERLINA, and SYASYA QONITA AZIZAH. "PENDEKATAN GLMM BINOMIAL NEGATIF DALAM MENGANALISIS KASUS KEMATIAN BAYI DI JAWA TIMUR TAHUN 2023." E-Jurnal Matematika 14, no. 1 (2025): 8. https://doi.org/10.24843/mtk.2025.v14.i01.p473.

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Infant Mortality Rate (IMR) is a critical indicator of the health and welfare of a community, particularly in East Java. This province has a concerning IMR, necessitating greater efforts to meet the Sustainable Development Goals (SDGs) target of 12 deaths per 1,000 lives births by 2023. Various factors contribute to infant mortality, including Low Birth Weight (LBW), limited exclusive breastfeeding, inadequate access to health services, low levels of maternal education, and economic disparities. These factors should be examined to understand their impact on the rising IMR. This study employs s

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Ponprisan, Phattarapon, Wongsa Loahasiriwong, Thitima Nutrawong, and Nopparat Senahad. "STIGMATIZATION AND QUALITY OF LIFE OF MALE-TO-FEMALE- TRANSGENDER UNIVERSITY STUDENTS IN THE NORTHEAST OF THAILAND." Journal of Southwest Jiaotong University 56, no. 5 (2021): 476–84. http://dx.doi.org/10.35741/issn.0258-2724.56.5.43.

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Transgender students are vulnerable to mental and physical health problems, impacting their quality of life (QOL). This research aims to study the stigma influence on the QOL of male-to-female transgender university students in Northeastern Thailand. This cross-sectional study was conducted among 765 male-to-female transgender students selected from 17 universities of the Northeast of Thailand using a multistage random sampling to respond to a self-administered structured questionnaire. The generalized linear mixed model (GLMM) was performed to identify factors associated with quality of life

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Moineddin, Rahim, Christopher Meaney, and Eva Grunfeld. "On the analysis of composite measures of quality in medical research." Statistical Methods in Medical Research 26, no. 2 (2014): 633–60. http://dx.doi.org/10.1177/0962280214553330.

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Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the

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Collins, Gavin, Jennifer P. Lundine, and Eloise Kaizar. "Bayesian Generalized Linear Mixed-Model Analysis of Language Samples: Detecting Patterns in Expository and Narrative Discourse of Adolescents With Traumatic Brain Injury." Journal of Speech, Language, and Hearing Research 64, no. 4 (2021): 1256–70. http://dx.doi.org/10.1044/2020_jslhr-20-00471.

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Purpose Generalized linear mixed-model (GLMM) and Bayesian methods together provide a framework capable of handling a wide variety of complex data commonly encountered across the communication sciences. Using language sample analysis, we demonstrate the utility of these methods in answering specific questions regarding the differences between discourse patterns of children who have experienced a traumatic brain injury (TBI), as compared to those with typical development. Method Language samples were collected from 55 adolescents ages 13–18 years, five of whom had experienced a TBI. We describe

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Overall, John E., and Scott Tonidandel. "Analysis of Data from a Controlled Repeated Measurements Design with Baseline-Dependent Dropouts." Methodology 3, no. 2 (2007): 58–66. http://dx.doi.org/10.1027/1614-2241.3.2.58.

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Abstract. Differences in mean rates of change are of primary interest in many controlled treatment evaluation studies. Generalized linear mixed model (GLMM) procedures are widely conceived to be the preferred method of analysis for repeated measurement designs when there are missing data due to dropouts, but systematic dependence of the dropout probabilities on antecedent or concurrent factors poses a problem for testing the significance of differences in mean rates of change across time in such designs. Controlling for the dependence of dropout probabilities on baseline values poses a special

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Kai, Mikihiko, James T. Thorson, Kevin R. Piner, and Mark N. Maunder. "Spatiotemporal variation in size-structured populations using fishery data: an application to shortfin mako (Isurus oxyrinchus) in the Pacific Ocean." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 11 (2017): 1765–80. http://dx.doi.org/10.1139/cjfas-2016-0327.

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We develop a length-disaggregated, spatiotemporal, delta-generalized linear mixed model (GLMM) and apply the method to fishery-dependent catch rates of shortfin mako sharks (Isurus oxyrinchus) in the North Pacific. The spatiotemporal model may provide an improvement over conventional time-series and spatially stratified models by yielding more precise and biologically interpretable estimates of abundance. Including length data may provide additional information to better understand life history and habitat partitioning for marine species. Nominal catch rates were standardized using a GLMM fram

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Saigusa, Yusuke, Shinto Eguchi, and Osamu Komori. "Generalized quasi-linear mixed-effects model." Statistical Methods in Medical Research, March 14, 2022, 096228022210858. http://dx.doi.org/10.1177/09622802221085864.

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The generalized linear mixed model (GLMM) is one of the most common method in the analysis of longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can occur when applying the GLMM. To address these issues, we extend the standard GLMM to a nonlinear mixed-effects model based on quasi-linear modeling. An estimation algorithm for the proposed model is provided by extending the penalized quasi-likelihood and the restricted maximum likelihood which are known in the GLMM inference. Also, the conditional AIC is formulated for the proposed mo

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Fokkema, Marjolein, and Achim Zeileis. "Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees." Behavior Research Methods, May 29, 2024. http://dx.doi.org/10.3758/s13428-024-02389-1.

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AbstractGrowth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to gro

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Madden, Laurence V., and Peter S. Ojiambo. "The value of generalized linear mixed models for data analysis in the plant sciences." Frontiers in Horticulture 3 (June 25, 2024). http://dx.doi.org/10.3389/fhort.2024.1423462.

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Modern data analysis typically involves the fitting of a statistical model to data, which includes estimating the model parameters and their precision (standard errors) and testing hypotheses based on the parameter estimates. Linear mixed models (LMMs) fitted through likelihood methods have been the foundation for data analysis for well over a quarter of a century. These models allow the researcher to simultaneously consider fixed (e.g., treatment) and random (e.g., block and location) effects on the response variables and account for the correlation of observations, when it is assumed that th

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Li, Wentao, Jiayi Tong, Md Monowar Anjum, Noman Mohammed, Yong Chen, and Xiaoqian Jiang. "Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources." BMC Medical Informatics and Decision Making 22, no. 1 (2022). http://dx.doi.org/10.1186/s12911-022-02014-1.

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Abstract Objectives This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package (‘lme4’) as the baseline model. Methods The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation,

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