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Journal articles on the topic 'GLMM'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Tran, M. N., N. Nguyen, D. Nott, and R. Kohn. "Bayesian Deep Net GLM and GLMM." Journal of Computational and Graphical Statistics 29, no. 1 (2019): 97–113. http://dx.doi.org/10.1080/10618600.2019.1637747.

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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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Glanzmann, Philipp, John Gustafson, Hithoshi Komatsuzawa, Kouji Ohta, and Brigitte Berger-Bächi. "glmM Operon and Methicillin-ResistantglmM Suppressor Mutants in Staphylococcus aureus." Antimicrobial Agents and Chemotherapy 43, no. 2 (1999): 240–45. http://dx.doi.org/10.1128/aac.43.2.240.

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ABSTRACT The Staphylococcus aureus phosphoglucosamine mutase gene glmM was shown to be the last gene of a three-cistron operon, orf1-orf2-glmM. One transcriptional start was identified upstream of orf1, and a second start producing a monocistronic transcript was identified upstream of glmM. Disruption of glmM abolished GlmM production, decreased methicillin resistance, and resulted in teicoplanin hypersusceptibility without affecting the production of the endogenous penicillin-binding proteins and PBP 2′. Complementation of the glmM mutation by the complete glmMoperon restored both methicillin

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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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Park, Hyun-Jeong, Dayeon Lee, and Hyeonju Kwon. "Fitting the GLMM tree to educational data: focusing on differential effects of afterschool program and private tutoring participation." Korean Society for Educational Evaluation 35, no. 4 (2022): 577–607. http://dx.doi.org/10.31158/jeev.2022.35.4.577.

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This study aims to introduce the GLMM tree and show its usefulness in educational research. The GLMM tree model is an extension of MOB to be applied to multilevel data, which detects subgroups with differential effects to estimate the fixed-effect in each subgroup and the random effect by the cluster to which each observation belongs. In this study, we identified the differential effects by detecting subgroups of afterschool programs and private tutoring participation in mathematics achievement of second grade high school students. Using the GLMM trees, students were divided into 16 and 15 sub

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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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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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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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Pasomboon, Pailin, Pramote Chumnanpuen, and Teerasak E-kobon. "Comparison of Hyaluronic Acid Biosynthetic Genes From Different Strains of Pasteurella multocida." Bioinformatics and Biology Insights 15 (January 2021): 117793222110274. http://dx.doi.org/10.1177/11779322211027406.

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Pasteurella multocida produces a capsule composed of different polysaccharides according to the capsular serotype (A, B, D, E, and F). Hyaluronic acid (HA) is a component of certain capsular types of this bacterium, especially capsular type A. Previously, 2 HA biosynthetic genes from a capsular type A strain were studied for the industrial-scale improvement of HA production. Molecular comparison of these genes across different capsular serotypes of P multocida has not been reported. This study aimed to compare 8 HA biosynthetic genes ( pgi, pgm, galU, hyaC, glmS, glmM, glmU, and hyaD) of 22 P

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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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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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Bayu, Suseno, Khairil Anwar Notodiputro, and Bagus Sartono. "GLMM and GLMMTree for Modelling Poverty in Indonesia." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 121–31. http://dx.doi.org/10.34123/icdsos.v2023i1.333.

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GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMM trees in real data applications of poverty in Indonesia. Using this data, we found that the GLMMTree algorithm method performs similarly to GLMM. 2 significant predictors affect poverty in Indonesia: the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding two variables, namely the percentage of households with electricity lighting access and the percentage of households with clean drinking wate

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Li, Yongmeng, Yan Zhou, Yufang Ma, and Xuebing Li. "Design and synthesis of novel cell wall inhibitors of Mycobacterium tuberculosis GlmM and GlmU." Carbohydrate Research 346, no. 13 (2011): 1714–20. http://dx.doi.org/10.1016/j.carres.2011.05.024.

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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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Sirodj, Dwi Agustin Nuriani, Khairil Anwar Notodiputro, and Bagus Sartono. "Perbandingan Kerja Binomial GLMM <i>Tree </i>dan BIMM <i>Forest </i>untuk Memodelkan Status Bekerja Penduduk." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 1 (2024): 95–106. http://dx.doi.org/10.25126/jtiik.20241117531.

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Model prediksi berbasis pada pohon keputusan saat ini banyak dikembangkan di berbagai bidang. Pengembangan metode yang dilakukan diantaranya memasukkan pengaruh acak ke dalam model. Generalized linier mixed model (GLMM) Tree menjadi salah satu model yang dapat mengakomodasi adanya pengaruh acak dan dilakukan dengan metode partisi rekursif hanya saja waktu komputasi yang dibutuhkan relatif lebih lama. Selanjutnya metode alternatif lainnya adalah Binary Mixed Model (BiMM) Forest yang menggabungkan prinsip kerja Bayesian GLMM dan Random Forest. Dari kedua metode yang akan digunakan maka permasala

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Rosenberger, Kristine J., Haitao Chu, and Lifeng Lin. "Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study." BMJ Open 12, no. 5 (2022): e055336. http://dx.doi.org/10.1136/bmjopen-2021-055336.

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ObjectivesSeveral methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate L

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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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Tavares, Isabel M., Laure Jolly, Frédérique Pompeo, et al. "Identification of the Pseudomonas aeruginosa glmM Gene, Encoding Phosphoglucosamine Mutase." Journal of Bacteriology 182, no. 16 (2000): 4453–57. http://dx.doi.org/10.1128/jb.182.16.4453-4457.2000.

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ABSTRACT A search for a potential algC homologue within thePseudomonas aeruginosa PAO1 genome database has revealed an open reading frame (ORF) of unknown function, ORF540 in contig 54 (July 1999 Pseudomonas genome release), that theoretically coded for a 445-amino-acid-residue polypeptide (I. M. Tavares, J. H. Leitão, A. M. Fialho, and I. Sá-Correia, Res. Microbiol. 150:105–116, 1999). The product of this gene is here identified as the phosphoglucosamine mutase (GlmM) which catalyzes the conversion of glucosamine-6-phosphate to glucosamine-1-phosphate, an essential step in the formation of

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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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Das, Kalyan, Mohamad Elmasri, and Arusharka Sen. "A Skew-normal copula-driven GLMM." Statistica Neerlandica 70, no. 4 (2016): 396–413. http://dx.doi.org/10.1111/stan.12092.

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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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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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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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Kilham, Philipp, Christoph Hartebrodt, and Gerald Kändler. "Generating Tree-Level Harvest Predictions from Forest Inventories with Random Forests." Forests 10, no. 1 (2018): 20. http://dx.doi.org/10.3390/f10010020.

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Wood supply predictions from forest inventories involve two steps. First, it is predicted whether harvests occur on a plot in a given time period. Second, for plots on which harvests are predicted to occur, the harvested volume is predicted. This research addresses this second step. For forests with more than one species and/or forests with trees of varying dimensions, overall harvested volume predictions are not satisfactory and more detailed predictions are required. The study focuses on southwest Germany where diverse forest types are found. Predictions are conducted for plots on which harv

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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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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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Elnosh, Maram, Hisham Altayb, Yousif Hamedelnil, et al. "Comparison of invasive histological and molecular methods in the diagnosis of Helicobacter pylori from gastric biopsies of Sudanese patients: a cross-sectional study." F1000Research 11 (January 28, 2022): 113. http://dx.doi.org/10.12688/f1000research.75873.1.

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Background: The continuous rise in the number of patients suffering from Helicobacter pylori is probably due to the changes in modern life. Nowadays, patients suffering from gastrointestinal problems are diagnosed through invasive and non-invasive techniques. The choice of a diagnostic test is influenced by factors such as the tests' sensitivity and specificity, the clinical conditions, and the cost-effectiveness of the testing strategy. This study aimed to compare molecular detection methods of H. pylori by polymerase chain reaction (PCR) targeting the 16S rRNA, ureA and glmM genes with an in

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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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Bernasconi, Juan F., María Raquel Perier, and Edgardo E. Di Giácomo. "Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?" Brazilian Journal of Oceanography 63, no. 2 (2015): 147–60. http://dx.doi.org/10.1590/s1679-87592015093606302.

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Abstract The catch per unit of effort (CPUE) data of cockfish, Callorhinchus callorynchus, during 1986-2011 was evaluated for the bottom trawl fishery of the San Matías gulf (Patagonia, Argentina). The objective of this work was to detect what are the factors related to fishery dynamic that affect catch rate of cockfish and to assess standardized CPUE by General linear models (GLMs) and General linear mixed models (GLMMs) as a relative abundance index. The annual trend of the catch rate indicated an increase during the evaluated period. The nominal CPUE and the indices standardized by the Delt

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Wolter, Riccarda, Volker Stefanski, and Konstanze Krueger. "Parameters for the Analysis of Social Bonds in Horses." Animals 8, no. 11 (2018): 191. http://dx.doi.org/10.3390/ani8110191.

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Social bond analysis is of major importance for the evaluation of social relationships in group housed horses. However, in equine behaviour literature, studies on social bond analysis are inconsistent. Mutual grooming (horses standing side by side and gently nipping, nuzzling, or rubbing each other), affiliative approaches (horses approaching each other and staying within one body length), and measurements of spatial proximity (horses standing with body contact or within two horse-lengths) are commonly used. In the present study, we assessed which of the three parameters is most suitable for s

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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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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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Díaz-Avalos, Carlos, David L. Peterson, Ernesto Alvarado, Sue A. Ferguson, and Julian E. Besag. "Spacetime modelling of lightning-caused ignitions in the Blue Mountains, Oregon." Canadian Journal of Forest Research 31, no. 9 (2001): 1579–93. http://dx.doi.org/10.1139/x01-089.

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Generalized linear mixed models (GLMM) were used to study the effect of vegetation cover, elevation, slope, and precipitation on the probability of ignition in the Blue Mountains, Oregon, and to estimate the probability of ignition occurrence at different locations in space and in time. Data on starting location of lightning-caused ignitions in the Blue Mountains between April 1986 and September 1993 constituted the base for the analysis. The study area was divided into a pixeltime array. For each pixeltime location we associated a value of 1 if at least one ignition occurred and 0 otherwise

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Payne, Elizabeth H., James W. Hardin, Leonard E. Egede, Viswanathan Ramakrishnan, Anbesaw Selassie, and Mulugeta Gebregziabher. "Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling." Statistical Methods in Medical Research 26, no. 4 (2015): 1802–23. http://dx.doi.org/10.1177/0962280215588569.

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Overdispersion is a common problem in count data. It can occur due to extra population-heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can lead to invalid inference. Our goal is to assess the differential performance of methods for dealing with overdispersion from several sources. We considered six different approaches: unadjusted Poisson regression (Poisson), deviance-scale-adjusted Poisson regression (DS-Poisson), Pearson-scale-adjusted Poisson regression (PS-Poisson), negative-binomial regression (NB), and two generalized linear mixed models (GLMM) wit

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Lee, Jinwoo, Youngsoon Kim, Gyemin Lee, and Hyunuk Seol. "A study on Regional Comparison for the Health Level using GLMM." Korean Data Analysis Society 24, no. 6 (2022): 2217–27. http://dx.doi.org/10.37727/jkdas.2022.24.6.2217.

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In this paper, we first examined the problem of the direct standardization method, which is mainly used to measure the regional health level by analyzing the data obtained from the Korea Community Health Survey, and proposed a new standardization method based on the Generalized Linear Mixed Model (GLMM). In order to evaluate the performance of the proposed standardization as an alternative to the existing direct standardization method, we used the data which obtained from the Korea Community Health Survey conducted in 2019. Using this data, we compared and analyzed three regional health level

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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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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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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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Nikoloulopoulos, Aristidis K. "A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3286–300. http://dx.doi.org/10.1177/0962280218796685.

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For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test,

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Ibrahim, Didymus, and Jared Mongare. "Modelling Claim Frequency with Spatial Effects for Accurate Insurance Premium Cost Calculations." Indonesian Journal of Applied Mathematics and Statistics 1, no. 2 (2024): 65–70. https://doi.org/10.71385/idjams.v1i2.13.

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This paper challenges the traditional assumption of independence between claim counts and amounts in non-life insurance. It explores the effectiveness of Generalized Linear Models (GLMs) in analyzing claim frequency data, a key component of accurate premium pricing. The proposed approach utilizes GLMs for both the marginal frequency and conditional severity of claims. Dependence between these factors is introduced by incorporating the number of claims as a covariate in the severity model. This strategy offers ease of implementation and interpretability, particularly when combined with Poisson

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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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Proudfoot, James A., Tuo Lin, Bokai Wang, and Xin M. Tu. "Tests for paired count outcomes." General Psychiatry 31, no. 1 (2018): e100004. http://dx.doi.org/10.1136/gpsych-2018-100004.

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For moderate to large sample sizes, all tests yielded pvalues close to the nominal, except when models were misspecified. The signed-rank test generally had the lowest power. Within the current context of count outcomes, the signed-rank test shows subpar power when compared with tests that are contrasted based on full data, such as the GEE. Parametric models for count outcomes such as the GLMM with a Poisson for marginal count outcomes are quite sensitive to departures from assumed parametric models. There is some small bias for all the asymptotic tests, that is,the signed-ranktest, GLMM and G

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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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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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Gauttam, Rahul, Christian K. Desiderato, Dušica Radoš, Hannes Link, Gerd M. Seibold, and Bernhard J. Eikmanns. "Metabolic Engineering of Corynebacterium glutamicum for Production of UDP-N-Acetylglucosamine." Frontiers in Bioengineering and Biotechnology 9 (September 23, 2021). http://dx.doi.org/10.3389/fbioe.2021.748510.

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Uridine diphosphate-N-acetylglucosamine (UDP-GlcNAc) is an acetylated amino sugar nucleotide that naturally serves as precursor in bacterial cell wall synthesis and is involved in prokaryotic and eukaryotic glycosylation reactions. UDP-GlcNAc finds application in various fields including the production of oligosaccharides and glycoproteins with therapeutic benefits. At present, nucleotide sugars are produced either chemically or in vitro by enzyme cascades. However, chemical synthesis is complex and non-economical, and in vitro synthesis requires costly substrates and often purified enzymes. A

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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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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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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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