Relevant bibliographies by topics / Generalised lineal mixed-effects models

Academic literature on the topic 'Generalised lineal mixed-effects models'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Generalised lineal mixed-effects models"

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Lai, T. L. "A hybrid estimator in nonlinear and generalised linear mixed effects models." Biometrika 90, no. 4 (December 1, 2003): 859–79. http://dx.doi.org/10.1093/biomet/90.4.859.

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Cadigan, N. G., M. J. Morgan, and J. Brattey. "Improved estimation and forecasts of stock maturities using generalised linear mixed models with auto-correlated random effects." Fisheries Management and Ecology 21, no. 5 (June 25, 2014): 343–56. http://dx.doi.org/10.1111/fme.12080.

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Willis, Brian H., Mohammed Baragilly, and Dyuti Coomar. "Maximum likelihood estimation based on Newton–Raphson iteration for the bivariate random effects model in test accuracy meta-analysis." Statistical Methods in Medical Research 29, no. 4 (June 11, 2019): 1197–211. http://dx.doi.org/10.1177/0962280219853602.

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A bivariate generalised linear mixed model is often used for meta-analysis of test accuracy studies. The model is complex and requires five parameters to be estimated. As there is no closed form for the likelihood function for the model, maximum likelihood estimates for the parameters have to be obtained numerically. Although generic functions have emerged which may estimate the parameters in these models, they remain opaque to many. From first principles we demonstrate how the maximum likelihood estimates for the parameters may be obtained using two methods based on Newton–Raphson iteration. The first uses the profile likelihood and the second uses the Observed Fisher Information. As convergence may depend on the proximity of the initial estimates to the global maximum, each algorithm includes a method for obtaining robust initial estimates. A simulation study was used to evaluate the algorithms and compare their performance with the generic generalised linear mixed model function glmer from the lme4 package in R before applying them to two meta-analyses from the literature. In general, the two algorithms had higher convergence rates and coverage probabilities than glmer. Based on its performance characteristics the method of profiling is recommended for fitting the bivariate generalised linear mixed model for meta-analysis.
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Hao, Chengcheng, Dietrich von Rosen, and Tatjana von Rosen. "Influence diagnostics for count data under AB–BA crossover trials." Statistical Methods in Medical Research 26, no. 6 (November 23, 2015): 2938–50. http://dx.doi.org/10.1177/0962280215615597.

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This paper aims to develop diagnostic measures to assess the influence of data perturbations on estimates in AB-BA crossover studies with a Poisson distributed response. Generalised mixed linear models with normally distributed random effects are utilised. We show that in this special case, the model can be decomposed into two independent sub-models which allow to derive closed-form expressions to evaluate the changes in the maximum likelihood estimates under several perturbation schemes. The performance of the new influence measures is illustrated by simulation studies and the analysis of a real dataset.
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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 (January 29, 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 logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.
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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 (January 29, 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 distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.
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Evans, Fiona H., and Jianxiu Shen. "Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning." Remote Sensing 13, no. 13 (June 22, 2021): 2435. http://dx.doi.org/10.3390/rs13132435.

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Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha−1, RMSE = 0.33 t ha−1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha−1, RMSE = 0.32 t ha−1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.
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Wehnert, Alexandra, Sven Wagner, and Franka Huth. "Effects of Pure and Mixed Pine and Oak Forest Stands on Carabid Beetles." Diversity 13, no. 3 (March 17, 2021): 127. http://dx.doi.org/10.3390/d13030127.

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The multiple-use approach to forestry applied in Germany aims to combine timber production and habitat management by preserving specific stand structures. We selected four forest stand types comprising (i) pure oak, (ii) equal oak–pine mixtures, (iii) single tree admixtures of oak in pine forest and (iv) pure pine. We analysed the effects of stand composition parameters on species representative of the larger carabid beetles (Carabus arvensis, C. coriaceus, C. hortensis, C. violaceus, Calosoma inquisitor). The main statistical methods used were correlation analyses and generalised linear mixed models. Cal. inquisitor was observed in pure oak forests exclusively. C. coriaceus and C. hortensis were absent from pure pine stands. High activity densities of C. arvensis and C. violaceus were observed in all four forest types. When assessed at the smaller scales of species crown cover proportions and spatial tree species effect zones, C. hortensis was found to be positively related to oak trees with a regular spatial distribution, whereas C. coriaceus preferred lower and more aggregated oak tree proportions. C. violaceus showed strong sex-specific tree species affinities. Information about preferences of carabid beetles is necessary for management activities targeting the adaptation of forest structures to habitat requirements.
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Adamec, Z. "Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship." Journal of Forest Science 61, No. 10 (June 3, 2016): 439–47. http://dx.doi.org/10.17221/68/2015-jfs.

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Tan, Z. D., L. R. Carrasco, and D. Taylor. "Corrigendum to: Spatial correlates of forest and land fires in Indonesia." International Journal of Wildland Fire 30, no. 9 (2021): 732. http://dx.doi.org/10.1071/wf20036_co.

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Biomass fires in Indonesia emit high levels of greenhouse gases and particulate matter, key contributors to global climate change and poor air quality in south-east Asia. In order to better understand the drivers of biomass fires across Indonesia over multiple years, we examined the distribution and probability of fires in Sumatra, Kalimantan (Indonesian Borneo) and Papua (western New Guinea) over four entire calendar years (2002, 2005, 2011 and 2015). The 4 years of data represent years with El Niño and La Niña conditions and high levels of data availability in the study region. Generalised linear mixed-effects models and zero-inflated negative binomial models were used to relate fire hotspots and a range of spatial predictor data. Geographic differences in occurrences of fire hotspots were evident. Fire probability was greatest in mixed-production agriculture lands and in deeper, degraded peatlands, suggesting anthropogenic activities were strong determinants of burning. Drought conditions in El Niño years were also significant. The results demonstrate the importance of prioritising areas of high fire probability, based on land use and other predisposing conditions, in effective fire management planning.
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Dissertations / Theses on the topic "Generalised lineal mixed-effects models"

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Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.

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Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of the residual variance for model uncertainty. Additionally, the general global linear approximation is extended to linear mixed-effect models to adjust the standard errors of the parameter estimates for model uncertainty. Both of these methods use a perturbation method for estimation, where random noise is added to the response variable and, conditional on the observed responses, the corresponding estimate is calculated. A simulation study demonstrates that when the proposed methodologies are utilized, both the variance and standard errors are inflated for model uncertainty. However, when a data-driven strategy is employed, the proposed methodologies show limited usefulness. These methods are evaluated with a trial assessing the performance of cervical traction in the treatment of cervical radiculopathy.
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Overstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.

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In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation. Default prior distributions for the model parameters, that are based on a unit information concept, are proposed. A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed. This strategy is applied to four examples throughout this thesis.
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Gory, Jeffrey J. "Marginally Interpretable Generalized Linear Mixed Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497966698387606.

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Min, Min. "Asymptotic normality in generalized linear mixed models." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7758.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Richardson, Troy E. "Treatment heterogeneity and potential outcomes in linear mixed effects models." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/15950.

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Doctor of Philosophy
Department of Statistics
Gary L. Gadbury
Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a treatment across the population. Consider a set of treatments, {T,C}, where T denotes some treatment that might be applied to an experimental unit and C denotes a control. For each of N experimental units, the duplet {r[subscript]i, r[subscript]Ci}, i=1,2,…,N, represents the potential response of the i[superscript]th experimental unit if treatment were applied and the response of the experimental unit if control were applied, respectively. The causal effect of T compared to C is the difference between the two potential responses, r[subscript]Ti- r[subscript]Ci. Much work has been done to elucidate the statistical properties of a causal effect, given a set of particular assumptions. Gadbury and others have reported on this for some simple designs and primarily focused on finite population randomization based inference. When designs become more complicated, the randomization based approach becomes increasingly difficult. Since linear mixed effects models are particularly useful for modeling data from complex designs, their role in modeling treatment heterogeneity is investigated. It is shown that an individual treatment effect can be conceptualized as a linear combination of fixed treatment effects and random effects. The random effects are assumed to have variance components specified in a mixed effects “potential outcomes” model when both potential outcomes, r[subscript]T,r[subscript]C, are variables in the model. The variance of the individual causal effect is used to quantify treatment heterogeneity. Post treatment assignment, however, only one of the two potential outcomes is observable for a unit. It is then shown that the variance component for treatment heterogeneity becomes non-estimable in an analysis of observed data. Furthermore, estimable variance components in the observed data model are demonstrated to arise from linear combinations of the non-estimable variance components in the potential outcomes model. Mixed effects models are considered in context of a particular design in an effort to illuminate the loss of information incurred when moving from a potential outcomes framework to an observed data analysis.
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Yam, Ho-kwan, and 任浩君. "On a topic of generalized linear mixed models and stochastic volatility model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B29913342.

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Ogden, Helen E. "Inference for generalised linear mixed models with sparse structure." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/60467/.

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The likelihood for the parameters of a generalised linear mixed model involves an integral which may be of very high dimension. Because of this apparent intractability, many alternative methods have been proposed for inference in these models, but it is shown that all can fail when the model is sparse, in that there is only a small amount of information available on each random effect. The sequential reduction method developed in this thesis seeks to fill in this gap, by exploiting the dependence structure of the posterior distribution of the random effects to reduce dramatically the cost of approximating the likelihood in models with sparse structure. Examples are given to demonstrate the high quality of the new approximation relative to the available alternatives. Finally, robustness of various estimators to misspecification of the random effect distribution is considered. It is found that certain marginal composite likelihood estimators are not robust to such misspecification in situations in which the full maximum likelihood estimator is robust, providing a counterexample to the notion that composite likelihood estimators will always be at least as robust as the maximum likelihood estimator under model misspecification.
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Tang, On-yee, and 鄧安怡. "Estimation for generalized linear mixed model via multipleimputations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30687652.

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Ma, Renjun. "An orthodox BLUP approach to generalized linear mixed models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0024/NQ38934.pdf.

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Tang, On-yee. "Estimation for generalized linear mixed model via multiple imputations." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30687652.

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Books on the topic "Generalised lineal mixed-effects models"

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Gbur, Edward E., Walter W. Stroup, Kevin S. McCarter, Susan Durham, Linda J. Young, Mary Christman, Mark West, and Matthew Kramer. Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2012. http://dx.doi.org/10.2134/2012.generalized-linear-mixed-models.

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Conference Board of the Mathematical Sciences. and National Science Foundation (U.S.), eds. Generalized linear mixed models. Beachwood, Ohio: Institute of Mathematical Statistics, 2003.

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McCulloch, Charles E. Generalized Linear Mixed Models. Beechwood OH and Alexandria VA: Institute of Mathematical Statistics and American Statistical Association, 2003. http://dx.doi.org/10.1214/cbms/1462106059.

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McCulloch, Charles E. Generalized, linear, and mixed models. New York: John Wiley & Sons, 2001.

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McCulloch, Charles E. Generalized, linear, and mixed models. 2nd ed. Hoboken, N.J: Wiley, 2008.

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McCulloch, Charles E. Generalized, Linear, and Mixed Models. New York: John Wiley & Sons, Ltd., 2005.

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Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Taylor & Francis, 2016.

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Extending linear models with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Chapman & Hall/CRC, 2006.

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Robert, Crouchley, ed. Multivariate generalized linear mixed models using R. Boca Raton, FL: CRC Press, 2011.

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John, Fox. Effects displays for generalized linear models. Toronto: York University, Institute for Social Research, 1987.

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Book chapters on the topic "Generalised lineal mixed-effects models"

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Mehtätalo, Lauri, and Juha Lappi. "Generalized Linear (Mixed-Effects) Models." In Biometry for Forestry and Environmental Data, 245–86. Boca Raton, FL : CRC Press, 2020. | Series: Chapman & Hall/CRC applied environmental statistics: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429173462-8.

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Hoff, Peter D. "Linear and generalized linear mixed effects models." In Springer Texts in Statistics, 195–207. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92407-6_11.

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Li, Chao, Lili Guo, Zheng Dou, Guangzhen Si, and Chunmei Li. "Generalized Multi-linear Mixed Effects Model." In Advances in Computer Science and Ubiquitous Computing, 253–58. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_41.

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Islam, M. Ataharul, and Rafiqul I. Chowdhury. "Generalized Linear Mixed Models." In Analysis of Repeated Measures Data, 169–76. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_13.

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Azen, Razia, and Cindy M. Walker. "Generalized Linear Mixed Models." In Categorical Data Analysis for the Behavioral and Social Sciences, 285–304. 2nd ed. Second edition. | New York, NY : Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9780429330308-11.

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Jiang, Jiming, and Thuan Nguyen. "Linear Mixed Models: Part I." In Linear and Generalized Linear Mixed Models and Their Applications, 1–61. New York, NY: Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1282-8_1.

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Jiang, Jiming, and Thuan Nguyen. "Linear Mixed Models: Part II." In Linear and Generalized Linear Mixed Models and Their Applications, 63–172. New York, NY: Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1282-8_2.

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Jiang, Jiming, and Thuan Nguyen. "Generalized Linear Mixed Models: Part II." In Linear and Generalized Linear Mixed Models and Their Applications, 235–317. New York, NY: Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1282-8_4.

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Jiang, Jiming, and Thuan Nguyen. "Generalized Linear Mixed Models: Part I." In Linear and Generalized Linear Mixed Models and Their Applications, 173–233. New York, NY: Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1282-8_3.

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Fahrmeir, Ludwig, and Gerhard Tutz. "Random Effects Models." In Multivariate Statistical Modelling Based on Generalized Linear Models, 283–329. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_7.

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Conference papers on the topic "Generalised lineal mixed-effects models"

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Yu, Dalei, Kelvin K. W. Yau, and Chang Ding. "Information Based Model Selection Criterion for Binary Response Generalized Linear Mixed Models." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.21.

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Li, Huayun, Laipeng Jin, and Dongchuan Yu. "Generalized Linear Mixed Models for the Analysis of Categorical Data." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3239438.3239461.

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Adnyani, Luh Putu Widya, Khairil Anwar Notodiputro, and Bagus Sartono. "Method generalized linear model and generalized linear mixed model for panel data Human Development Index (HDI) in Indonesia." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0111780.

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Anggara, Dimas, Khairil Anwar Notodiputro, and Bagus Sartono. "Generalized linear mixed models: Application for consumer price index in Indonesia." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108883.

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Xingcai Zhou. "Monte Carlo EM algorithm for generalized linear models with linear structural random effects." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5764054.

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Zhou, Jiayu, Qi Li, E. Mingcheng, Zengqiang Jiang, and Jing Ma. "Train Wheel Rim Degradation Modeling based on Generalized Linear Mixed Effect Model." In 2019 Prognostics and System Health Management Conference (PHM-Qingdao). IEEE, 2019. http://dx.doi.org/10.1109/phm-qingdao46334.2019.8943064.

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Xie, Shengkun, Chong Gan, and Clare Chua-Chow. "Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models." In 10th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010601003290334.

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Xie, Shengkun, Chong Gan, and Clare Chua-Chow. "Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models." In 10th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010601000002993.

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Tonah, Tonah, Anang Kurnia, and Kusman Sadik. "Hierarchical Generalized Linear Mixed Models for Multilevel Analysis of Indonesian Student’s PISA Mathematics Literacy Achievement." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290468.

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Sunethra, A. A., and M. R. Sooriyarachchi. "Use of Sandwich Variance Estimation in Generalized Linear Mixed Models: for Binary Repeated Measures Data." In Annual International Conference on Operations Research and Statistics ( ORS 2016 ). Global Science & Technology Forum ( GSTF ), 2016. http://dx.doi.org/10.5176/2251-1938_ors16.12.

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Reports on the topic "Generalised lineal mixed-effects models"

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Juricek, Ben C. Generalized Linear Mixed-Effects Models in R. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada413561.

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Comola, Margherita, Rokhaya Dieye, and Bernard Fortin. Heterogeneous peer effects and gender-based interventions for teenage obesity. CIRANO, September 2022. http://dx.doi.org/10.54932/tqag9043.

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This paper explores the role of gender heterogeneity in the social diffusion of obesity among adolescents and its policy implications. We propose a generalized linear social interaction model which allows for gender-dependent heterogeneity in peer effects through the channel of social synergy. We estimate the model using data on adolescent Body Mass Index and network-based interactions. Our results show that peer effects are gender-dependent, and male students are particularly responsive to the weight of their female friends. Our simulations indicate that female-tailored interventions are likely to be more effective than a gender-neutral approach to fight obesity in schools.
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