Relevant bibliographies by topics / Generalized linear mixed model (GLMM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Generalized linear mixed model (GLMM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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

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Garrido, José, and Jun Zhou. "Full Credibility with Generalized Linear and Mixed Models." ASTIN Bulletin 39, no. 1 (May 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 mechanism to obtain confidence intervals for the GLM and GLMM estimators.
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Fox, Jean-Paul, Duco Veen, and Konrad Klotzke. "Generalized Linear Mixed Models for Randomized Responses." Methodology 15, no. 1 (January 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 individual’s response and the sensitive question, thereby protecting the individual’s privacy. Next to the development of the innovative data collection methods, methodological advances have been made to enable a multivariate analysis to relate responses to sensitive questions to other variables. It is shown that the developments can be represented by a general response probability model (including all common designs) by extending it to a generalized linear model (GLM) or a generalized linear mixed model (GLMM). The general methodology is based on modifying common link functions to relate a linear predictor to the randomized response. This approach makes it possible to use existing software for GLMs and GLMMs to model randomized response data. The R-package GLMMRR makes the advanced methodology available to applied researchers. The extended models and software will seriously improve the application of the randomized response methodology. Three empirical examples are given to illustrate the methods.
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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 (April 24, 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 data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021
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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 (July 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 the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation–Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects. Results Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package. Availability and implementation The software is released in open source at https://github.com/huthvincent/cGLMM. Supplementary information Supplementary data are available at Bioinformatics online.
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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 (June 27, 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 Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.
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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 (February 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 the context of GLMM. The estimator was tested through Monte Carlo simulation and compared with a regular generalized linear model (GLM). The estimator was also applied to a real-world case study, a harvest model. The results showed that GLM predictions were unbiased but that their confidence intervals did not achieve their nominal coverage. On the other hand, the proposed estimator yielded unbiased predictions with reliable confidence intervals. The predictions based on the fixed effects of a GLMM exhibited the largest biases. If statistical inferences are needed, the proposed estimator should be used. It is easily implemented as long as the random effect specification does not contain multiple random effects for the same hierarchical level.
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MAIORANO, Amanda Marchi, Thiago Santos MOTA, Ana Carolina VERDUGO, Ricardo Antonio da Silva FARIA, Beatriz Pressi Molina da SILVA, Márcia Cristina de Sena OLIVEIRA, Joslaine Noely dos Santos Gonçalves CYRILLO, and Josineudson Augusto II de Vasconcelos SILVA. "COMPARATIVE STUDY OF CATTLE TICK RESISTANCE USING GENERALIZED LINEAR MIXED MODELS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 1 (March 25, 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 evaluation of tick count data, the GLMMs. Analysis for tick resistance by GLMM with Negative binomial distribution has never been assessed previously. The analyses were performed by the use of the PROC GLIMMIX procedure of the SAS program. The results showed that GLMM with Negative binomial distribution is appropriated to evaluate tick count data with excess of zero observations avoiding overdispersion problems. Finally, considering multiple comparisons with the Bonferroni test, different pattern of tick infestation was observed for the studied breeds, suggesting that NE is the most resistant breed followed by CA.
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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 (September 12, 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 models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.
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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 (July 31, 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 performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)
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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 (March 8, 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, must be used with either GLMMs or GEEs when developing RSFs. There are several important differences between these approaches; in particular, GLMMs are best for producing parameter estimates that predict how management might influence individuals, while GEEs are best for predicting how management might influence populations. As the interpretation, value, and statistical significance of both types of parameter estimates differ, it is important that users select the appropriate analytical method. We also outline the use of k-fold cross validation to assess fit of these models. Both GLMMs and GEEs hold promise for developing RSFs as long as they are used appropriately.
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Dissertations / Theses on the topic "Generalized linear mixed model (GLMM)"

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Nuthmann, Antje, Wolfgang Einhäuser, and Immo Schütz. "How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models." Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232614.

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Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.
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Carvalho, Rafael Augusto Pincante de. "Fatores determinantes da intensidade de uso dos abrigos pela geneta (Genetta genetta L. 1758) numa região mediterrânica." Master's thesis, Universidade de Évora, 2012. http://hdl.handle.net/10174/15506.

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A disponibilidade de locais de abrigo é um elemento chave para a persistência e conservação de populações de carnívoros. Este trabalho avaliou a influência de gradientes ecológicos na seleção e intensidade de uso de abrigos pela geneta. Foram caracterizados os abrigos de 21 genetas sujeitas a radioseguimento diário, entre Maio de 2010 e Janeiro de 2012. Usaram-se modelos lineares mistos para modelar 4 gradientes ecológicos definidos com base numa Análise de Componentes Principais a partir das variáveis explicativas originais. A tranquilidade (31%), a heterogeneidade da paisagem (23%), o relevo (17%) e a insolação (10%) explicaram 81% da variância na proporção de uso dos abrigos. As genetas usam mais abrigos em árvores, localizados em áreas tranquilas, expostos a sul, com um relevo moderadamente acidentado em zonas homogéneas de montado ou ninhos construídos na vegetação em áreas mais diversas do ponto de vista paisagístico, perto de ribeiras com galeria ripícola. A incorporação desta informação, na gestão de áreas florestais, possibilitará manter a qualidade e diversidade dos abrigos, condição necessária à viabilidade futura das populações dos pequenos carnívoros florestais; ABSTRACT:The availability of resting sites is a key element for the conservation and persistence of wild carnivore populations. Our study aimed to describe how the ecological gradients influence the selection and intensity of use of resting sites by the genet. We characterized the resting sites of 21 common genets followed by radio telemetry between May 2010 and January 2012, on a daily basis. Generalized linear mixed models were used to model 4 ecological gradients obtained from a Principal Component Analysis based on the original explanatory variables . Tranquillity (31%), landscape heterogeneity (23%), landscape roughness (17%) and insolation (10 %) explained 81% of the variance in the resting site selection and intensity use. Genets use mainly tree hollows, located on quiet places, south orientated, in moderately hilly areas of homogeneous montado or they use vegetation nests in more heterogeneous landscapes located near watercourses with riparian gallery. The incorporation of this information on forest management plans will maintain the quality and diversity of resting sites, which are key conditions for the future viability of small forest carnivores.
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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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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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Sepato, Sandra Moepeng. "Generalized linear mixed model and generalized estimating equation for binary longitudinal data." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/43143.

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The most common analysis used for binary data is generalised linear model (GLM) with either a binomial or bernoulli distribution using either a logit, probit, complementary log-log or other type of link functions. However, such analyses violate the independence assumption if the binary data are measured repeatedly over time at the same subject or site. Failure to take into account the correlation can lead to incorrect estimation of regression parameters and the estimates are less efficient, particularly when the correlations are large. Therefore, to obtain the most efficient estimates that are also unbiased the methods that incorporate correlations (McCullagh and Nelder, 1989) should be used. Two of the statistical methodologies that can be used to account for this correlation for the longitudinal data are the generalized linear mixed models (GLMMs) and generalized estimating equation (GEE). The GLMM method is based on extending the fixed effects GLM to include random effects and covariance patterns. Unlike the GLM and GLMM methods, the GEE method is based on the quasi-likelihood theory and no assumption is made about the distribution of response observations (Liang and Zeger, 1986). The main objective of the study is to investigate the statistical properties and limitations of these three approaches, i.e. GLM, GLMMs and GEE for analyzing longitudinal data through use of a binary data from an entomology study. The results reaffirms the point made by these authors that misspecification of working correlation in GEE approach would still give consistent regression parameter estimates. Further, the results of this study suggest that even with small correlation, ignoring a random effects in a binary model can lead to inconsistent estimation.
Dissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
Unrestricted
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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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Chen, Jinsong. "Semiparametric Methods for the Generalized Linear Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28012.

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The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution. In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood approach is proposed to estimate the nonparametric function and parameters including single-index coe±cients in the GSSIMM. We estimate variance components using marginal quasi-likelihood. Asymptotic properties of the estimators are developed using a similar idea by Yu (2008). A simulation example is carried out to compare the performance of the GSSIMM with that of the GLMM. We demonstrate the advantage of my approach using a study of the association between daily air pollutants and daily mortality adjusted for temperature and wind speed in various counties of North Carolina.
Ph. D.
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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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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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Zhan, Tingting. "The Generalized Linear Mixed Model for Finite Normal Mixtures with Application to Tendon Fibrilogenesis Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/171613.

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Statistics
Ph.D.
We propose the generalized linear mixed model for finite normal mixtures (GLMFM), as well as the estimation procedures for the GLMFM model, which are widely applicable to the hierarchical dataset with small number of individual units and multi-modal distributions at the lowest level of clustering. The modeling task is two-fold: (a). to model the lowest level cluster as a finite mixtures of the normal distribution; and (b). to model the properly transformed mixture proportions, means and standard deviations of the lowest-level cluster as a linear hierarchical structure. We propose the robust generalized weighted likelihood estimators and the new cubic-inverse weight for the estimation of the finite mixture model (Zhan et al., 2011). We propose two robust methods for estimating the GLMFM model, which accommodate the contaminations on all clustering levels, the standard-two-stage approach (Chervoneva et al., 2011, co-authored) and a robust joint estimation. Our research was motivated by the data obtained from the tendon fibril experiment reported in Zhang et al. (2006). Our statistical methodology is quite general and has potential application in a variety of relatively complex statistical modeling situations.
Temple University--Theses
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Books on the topic "Generalized linear mixed model (GLMM)"

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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 the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition. Taylor & Francis Group, 2016.

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science). Chapman & Hall/CRC, 2005.

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Walsh, Bruce, and Michael Lynch. Short-term Changes in the Mean: 2. Truncation and Threshold Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0014.

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The selection intensity, the mean change in a trait within a generation expressed in phenotypic standard deviations, provides an important metric for comparing the strength of selection over designs. Further, under truncation selection (only individuals above some threshold leave offspring), the selection intensity is a function of the fraction saved, and hence the breeder's equation is often expressed in terms of the selection intensity. An important special case of truncation selection is a threshold trait, wherein an individual only expresses a particular phenotype when its underlying liability value exceeds some threshold. This chapter examines selection on such traits, and generalizes this binary-trait setting (with binomial residuals) to other classes of discrete traits, wherein some underling linear model (generating the threshold) is this transformed via a generalized linear mixed model into an observed trait value.
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Book chapters on the topic "Generalized linear mixed model (GLMM)"

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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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Cai, Bo, and David B. Dunson. "Bayesian Variable Selection in Generalized Linear Mixed Models." In Random Effect and Latent Variable Model Selection, 63–91. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_4.

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Sutradhar, B. C., and V. P. Godambe. "On Estimating Function Approach in the Generalized Linear Mixed Model." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 193–214. Hayward, CA: Institute of Mathematical Statistics, 1997. http://dx.doi.org/10.1214/lnms/1215455046.

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Carrasquinha, Eunice, M. Helena Gonçalves, and M. Salomé Cabral. "Generalized Linear Mixed Effects Model in the Analysis of Longitudinal Discrete Data." In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, 113–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34904-1_11.

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Zhang, Daowen, and Xihong Lin. "Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics." In Random Effect and Latent Variable Model Selection, 19–36. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_2.

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Givens, Geof H., J. Ross Beveridge, Bruce A. Draper, and David Bolme. "Using a Generalized Linear Mixed Model to Study the Configuration Space of a PCA+LDA Human Face Recognition Algorithm." In Articulated Motion and Deformable Objects, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30074-8_1.

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Burton, Paul, Lyle Gurrin, and Peter Sly. "Clustered Data: Extending the Simple Linear Regression Model to Account for Correlated Responses: An Introduction to Generalized Estimating Equations and Multi-Level Mixed Modelling." In Tutorials in Biostatistics, 1–33. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023724.ch1a.

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Yang, Yang, and Kenneth C. Land. "Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework." In Age-Period-Cohort Analysis, 55–73. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b13902-4.

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"Chapter 7: Maximum likelihood for GLMMs." In Generalized Linear Mixed Models, 48–56. Beechwood OH and Alexandria VA: Institute of Mathematical Statistics and American Statistical Association, 2003. http://dx.doi.org/10.1214/cbms/1462106067.

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"Chapter 3: Generalized linear models (GLMs)." In Generalized Linear Mixed Models, 21–27. Beechwood OH and Alexandria VA: Institute of Mathematical Statistics and American Statistical Association, 2003. http://dx.doi.org/10.1214/cbms/1462106063.

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Conference papers on the topic "Generalized linear mixed model (GLMM)"

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Handayani, Dian, Khairil Anwar Notodiputro, Kusman Sadik, and Anang Kurnia. "A comparative study of approximation methods for maximum likelihood estimation in generalized linear mixed models (GLMM)." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979449.

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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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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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Widyaningsih, Yekti, Asep Saefuddin, Khairil A. Notodiputro, and Aji H. Wigena. "Nested generalized linear mixed model with ordinal response: Simulation and application on poverty data in Java Island." In THE 5TH INTERNATIONAL CONFERENCE ON RESEARCH AND EDUCATION IN MATHEMATICS: ICREM5. AIP, 2012. http://dx.doi.org/10.1063/1.4724132.

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Saengngam, Nikorn, and Unchalee Thonggumnead. "Predicting the medium-term electricity load demand of Thailand using the generalized estimating equation and the linear mixed effect model." In 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2015. http://dx.doi.org/10.1109/ecticon.2015.7206994.

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You might also be interested in the extended bibliographies on the topic 'Generalized linear mixed model (GLMM)' for particular source types:
Journal articles Dissertations / Theses
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