Дисертації з теми "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.
Повний текст джерелаOverstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.
Повний текст джерелаGory, Jeffrey J. "Marginally Interpretable Generalized Linear Mixed Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497966698387606.
Повний текст джерелаMin, Min. "Asymptotic normality in generalized linear mixed models." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7758.
Повний текст джерелаThesis 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.
Richardson, Troy E. "Treatment heterogeneity and potential outcomes in linear mixed effects models." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/15950.
Повний текст джерелаDepartment 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.
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.
Повний текст джерелаOgden, Helen E. "Inference for generalised linear mixed models with sparse structure." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/60467/.
Повний текст джерелаTang, 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.
Повний текст джерелаMa, 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.
Повний текст джерелаTang, 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.
Повний текст джерелаSepato, 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.
Повний текст джерелаDissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
Unrestricted
Hercz, Daniel. "Flexible modeling with generalized additive models and generalized linear mixed models: comprehensive simulation and case studies." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114300.
Повний текст джерелаCette these compare des GAM et GLMM dans le cadre de la modélisation des courbes non-linéaires. L'étude comprend une simulation complète et quelques analyses réelles. La simulation utilise des milliers de 'datasets' générés pour comparer forme entres les deux modèles (et les modèles linéaires comme point de repère), l'étendue de la non-linéarité, et la forme de la courbe obtenue. Les analyses d'étendre les résultats de la simulation à courbes de la fonction pulmonaire avec de GLMM / GAM avec mesures du tabagisme (la variable indépendante). Un autre analyse réelle avec les résultats dichotomiques complète l'étude et que les résultats soient plus représentatifs.
Chen, Jinsong. "Semiparametric Methods for the Generalized Linear Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28012.
Повний текст джерелаPh. D.
Hossain, Mohammad Zakir. "A small-sample randomization-based approach to semi-parametric estimation and misspecification in generalized linear mixed models." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/24641.
Повний текст джерелаShrewsbury, John Stephen. "Calibration of trip distribution by generalised linear models." Thesis, University of Canterbury. Department of Civil and Natuaral Resources Engineering, 2012. http://hdl.handle.net/10092/7685.
Повний текст джерелаNelson, Kerrie P. "Generalized linear mixed models : development and comparison of different estimation methods /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/8960.
Повний текст джерелаChen, Yin. "Quasi-Monte Carlo methods in generalized linear mixed model with correlated and non-normal random effects." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516829.
Повний текст джерелаBarbosa, Luciano [UNESP]. "Metodologias estatísticas na análise de germinação de sementes de mamona." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/101848.
Повний текст джерелаÉ bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados
Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data
Evangelou, Evangelos A. Smith Richard L. "Bayesian and frequentist methods for approximate inference in generalized linear mixed models." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2607.
Повний текст джерелаTitle from electronic title page (viewed Oct. 5, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research Statistics." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
Jung, Jungah. "Using generalized linear models with a mixed random component to analyze count data." Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/JungJX2001.pdf.
Повний текст джерелаKurusu, Ricardo Salles. "Avaliação de técnicas de diagnóstico para a análise de dados com medidas repetidas." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21062013-202727/.
Повний текст джерелаConditional and marginal models are among the possibilities in statistical literature to analyze data from studies with correlated observations. Several techniques have been proposed for diagnostic analysis in these models. The objective of this work is to present some of the diagnostic techniques available for both modeling approaches and to evaluate them by simulation studies. The presented techniques were also applied in a real dataset.
Maekawa, Eduardo Shigueiti. "Estimativa do custo da colheita mecanizada de cana-de-açúcar utilizando modelos de regressão." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-30092016-101059/.
Повний текст джерелаThe mechanized harvesting of sugarcane is one of the most significant and costly operations of the production process, thus it is important to understand the relationships involving its cost. Currently, methods to estimate these costs rise from the concept of fixed and variable cost. However, considering the complexity of the harvesting process, it is necessary to evaluate techniques to relate the operating parameters with the final cost. In this context, statistical modeling by regression allows to treat such relationship and predict trends. The objective of this study was to develop an empirical model to calculate the cost of mechanical harvesting of sugarcane. A generalized linear model (GLM) and a generalized linear mixed model (GLMM) both with gamma distribution was developed using operational indicators and cost data from 20 plants in the sugarcane industry. Through the GLMM, satisfactory adhesion was obtained when compared to the GLM, null model (average) and linear (assuming normality). The indicators that explained the cost were: productivity (t mach-1), consumption (l t-1), hourmeter (h) and number of operators per harvester (nop).
Barbosa, Luciano 1971. "Metodologias estatísticas na análise de germinação de sementes de mamona /." Botucatu : [s.n.], 2010. http://hdl.handle.net/11449/101848.
Повний текст джерелаBanca: Liciana Vaz da Arruda
Banca: Osmar Delmanto Junior
Banca: Célia Regina Lopes Zimback
Banca: Marli Teixeira de A. Minhoni
Resumo: É bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados
Abstract: Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data
Doutor
Codd, Casey. "A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.
Повний текст джерелаCHEN, JUNLIANG. "A MONTE CARLO EM ALGORITHM FOR GENERALIZED LINEAR MIXED MODELS WITH FLEXIBLE RANDOMEFFECTS DISTRIBUTION." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20011025-112332.
Повний текст джерелаCHEN, JUNLIANG. A Monte Carlo EM algorithm for generalized linear mixed modelswith flexible random effects distribution. (Under the direction of DaowenZhang and Marie Davidian)A popular way to model correlated binary, count, or other data arising inclinical trials and epidemiological studies of cancer and other diseases is byusing generalized linear mixed models (GLMMs), which acknowledge correlationthrough incorporation of random effects. A standard model assumption is thatthe random effects follow a parametric family such as the normal distribution.However, this may be unrealistic or too restrictive to represent the data,raising concern over the validity of inferences both on fixed and randomeffects if it is violated.Here we use the seminonparametric (SNP) approach (Davidian and Gallant 1992,1993) to model the random effects, which relaxes the normality assumption andjust requires that the distribution of random effects belong to a class of``smooth'' densities given by Gallant and Nychka (1987). This representation allows the density of random effects to be very flexible, including densitiesthat are skewed, multi--modal, fat-- or thin--tailed relative to the normal, andthe normal as a special case. We also provide a reparameterization of thisrepresentation to avoid numerical instability in estimating the polynomialcoefficients.Because an efficient algorithm to sample from a SNP density is available, wepropose a Monte Carlo expectation maximization (MCEM) algorithm using arejection sampling scheme (Booth and Hobert, 1999) to estimate the fixedparameters of the linear predictor, variance components and the SNP density. Astrategy of choosing the degree of flexibility required for the SNP density isalso proposed. We illustrate the methods by application to two data sets fromthe Framingham and Six Cities Studies, and present simulations demonstratingperformance of the approach.
Beauchamp, Marie-Eve. "Generalized linear mixed models for binary outcome data with a low proportion of occurrences." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86709.
Повний текст джерелаTo begin with, I considered graphical representations of the distributions of cluster-specific log odds of outcome ensuing from random intercepts logistic models (RILMs) converted to the probability scale with the inverse logit transformation. RILMs are special cases of GLMMs. These representations are helpful to comprehend the implications of RILM parameter values for the distributions of cluster-specific probabilities of outcome. The correspondence of these distributions with beta distributions, also used for random effects models for binary outcomes, was graphically assessed and a generally good agreement was found.
Afterwards, I evaluated via a simulation study the performance of the PQL and AGHQ methods in several realistic settings of binary outcome data with a low proportion of occurrences. Different features determining the number of occurrences were considered (number of clusters, cluster size, and probabilities of outcome). The AGHQ method produced nearly unbiased fixed effects estimates, even in challenging settings with low proportions of occurrences or a small sample size, but mean square errors tended to be larger than with PQL for small datasets. Both methods produced biased variance component estimates when the number of clusters was moderate, especially with rarer occurrences.
Finally, through further analysis of the simulation results, I assessed if a number of indicators quantifying different aspects of the rarity of the events in a dataset, all measurable in practice, could explain patterns of bias in the parameter estimates. The selected rarity indicators quantify the overall number of events and their distribution across the clusters.
Plusieurs études en épidémiologie et autres domaines, tels que les sciences sociales, donnent lieu à des données de réponse corrélées (par exemple, les études longitudinales et multi-centres). L'estimation des paramètres des modèles linéaires généralisés mixtes (MLGM), souvent utilisés pour les données de réponse corrélées, est compliquée par des intégrales sans solution analytique dans la fonction de vraisemblance marginale. La méthode de quasi-vraisemblance pénalisée (QVP) et l'estimation par la maximisation de la vraisemblance conjointement avec la technique d'intégration numérique de quadrature Gauss-Hermite adaptée (QGHA) sont souvent utilisées. Cependant, l'évaluation de la performance de ces méthodes en pratique est incomplète, en particulier pour les données de réponse binaires avec faible proportion d'événements.
Dans un premier temps, j'ai considéré la représentation graphique de distributions du logarithme de la cote spécifique à chaque groupe résultant de modèles logistiques avec intercepts aléatoires (MLIA) transformées à l'échelle des probabilités avec la transformation logit inversée. Les MLIA sont des cas particuliers des MLGM. Ces représentations sont utiles pour comprendre les implications des valeurs des paramètres sur la distribution de la probabilité de réponse spécifique à chaque groupe. La correspondance avec la loi bêta a été évaluée graphiquement et une bonne concordance fut observée.
Par la suite, j'ai évalué avec une étude de simulations la performance des méthodes QVP et QGHA pour plusieurs cas réalistes de données de réponse binaires avec faible proportion d'événements. Différentes caractéristiques déterminant le nombre d'événements furent considérées (nombre et taille des groupes et probabilités d'événement). La méthode QGHA a produit des valeurs estimées presque sans biais, même dans des situations avec faible proportion d'événements ou petite taille d'échantillon, mais les erreurs quadratiques moyennes étaient souvent plus élevées qu'avec la méthode QVP pour les petits échantillons. Les deux méthodes ont produit des valeurs estimées biaisées pour la composante de variance lorsque le nombre de groupes était modéré, particulièrement lorsque les événements étaient rares.
Finalement, j'ai évalué si un nombre d'indicateurs de rareté des événements, tous mesurables en pratique pour un jeu de données, pouvaient expliquer le biais dans les valeurs estimées des paramètres. Les indicateurs sélectionnés quantifient le nombre total d'événements et leur distribution dans les groupes.
Cho, Jang Ik. "Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999.
Повний текст джерелаZhan, 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.
Повний текст джерелаPh.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
Hewson, Paul James. "On the uses of generalised linear mixed models for the simultaneous investigation of multiple performance indicators." Thesis, University of Exeter, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418464.
Повний текст джерелаPolicastro, Catherine. "The Effects of Ecological Context and Individual Characteristics on Stereotyped Displays in Male Anolis carolinensis." ScholarWorks@UNO, 2013. http://scholarworks.uno.edu/td/1757.
Повний текст джерелаNuthmann, 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.
Повний текст джерелаShannon, Carlie. "A case study in applying generalized linear mixed models to proportion data from poultry feeding experiments." Kansas State University, 2013. http://hdl.handle.net/2097/15519.
Повний текст джерелаDepartment of Statistics
Leigh Murray
This case study was motivated by the need for effective statistical analysis for a series of poultry feeding experiments conducted in 2006 by Kansas State University researchers in the department of Animal Science. Some of these experiments involved an automated auger feed line system commonly used in commercial broiler houses and continuous, proportion response data. Two of the feed line experiments are considered in this case study to determine if a statistical model using a non-normal response offers a better fit for this data than a model utilizing a normal approximation. The two experiments involve fixed as well as multiple random effects. In this case study, the data from these experiments is analyzed using a linear mixed model and Generalized Linear Mixed Models (GLMM’s) with the SAS Glimmix procedure. Comparisons are made between a linear mixed model and GLMM’s using the beta and binomial responses. Since the response data is not count data a quasi-binomial approximation to the binomial is used to convert continuous proportions to the ratio of successes over total number of trials, N, for a variety of possible N values. Results from these analyses are compared on the basis of point estimates, confidence intervals and confidence interval widths, as well as p-values for tests of fixed effects. The investigation concludes that a GLMM may offer a better fit than models using a normal approximation for this data when sample sizes are small or response values are close to zero. This investigation discovers that these same instances can cause GLMM’s utilizing the beta response to behave poorly in the Glimmix procedure because lack of convergence issues prevent the obtainment of valid results. In such a case, a GLMM using a quasi-binomial response distribution with a high value of N can offer a reasonable and well behaved alternative to the beta distribution.
Nati, Lilian. "Superdispersão em dados binomiais hierárquicos." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-19062008-132744/.
Повний текст джерелаA common alternative when analyzing binary data originated from a two-level hierarchical structure (for instance, student and school) is to assume a binomial distribution for the experimental units of the first level (student) conditionally to a normal random effect for the second level units (school). In this work, we propose the inclusion of a second normal random effect in the first level to contemplate a possible extra-binomial variability due to the dependence among the Bernoulli trials in the same individual. We obtain the maximum likelihood estimation process for this hierarchical model starting from the marginal likelihood of the data, after a double application of the adaptive Gauss-Hermite quadrature as an approximation of the integrals of the random effects. We conduct a simulation study to compare the inferential properties of the advocated model with the generalized linear (binomial) model, a quasi-likelihood model and the usual two-level hierarchical generalized linear model.
Fatoretto, Maíra Blumer. "Modelos para dados categorizados ordinais com efeito aleatório: uma aplicação à análise sensorial." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-16032016-170135/.
Повний текст джерелаModels for ordinal categorical data are extensions of the Generalized Linear Models and their assumptions and inferences are based on this class of models. The Cumulative Logit Models in wich the link function consists of accumulated probabilities are more used for this type of variable, with one of its simplifications are the Proportional Odds Model, in wich for all covariates in the model there is a linear growth in odds ratios, but in this case, checking the parallelism assumption is required. Other models such as the Partial Proportional Odds Model, the Adjacent-Categories Logits and Continuation-Ratio Logits model can also be used. In several of such studies, the use of mixed models is required, either by type of factor or dependence between the response variable observations. The aim of this work is studying models for ordinal variable response with the inclusion of one or more random effects. These models are illustrated by using real data of sensory analysis, the response variable consists of an ordinal scale and we want to know from two varieties of dried tomatoes, Italian and Sweet Grape, which had better acceptance by consumers. In this experiment, the panelists evaluated each variety once, and the repetitions constituted by the ratings given by different tasters. In this case, the inclusion of a random effect by taster is required so that the model can capture the difference between these untrained tasters. The Proportional Odds Model fitted satisfactorily to the data and it is possible to make use of the estimates of probabilities and odds ratios for the interpretation of results and concluding that the taste of the variety Sweet Grape was the one that most pleased the tasters regardless of sex.
Hao, Chengcheng. "Explicit Influence Analysis in Crossover Models." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-107703.
Повний текст джерелаMenezes, Renee Xavier de. "More useful standard errors for group and factor effects in generalized linear models." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302362.
Повний текст джерелаHu, Shuwen. "Statistical modeling and machine learning in longitudinal data analysis." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211253/1/Shuwen_Hu_Thesis.pdf.
Повний текст джерелаPinto, João Pedro Senhorães Senra. "New credibility approaches in workers compensation insurance." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10853.
Повний текст джерелаNo nosso relatório apresentamos diferentes interpretações da teoria de credibilidade de Bühlmann que foram aplicadas na análise da carteira de seguros de trabalho de uma seguradora portuguesa. Começamos pela apresentação e implementação dos modelos clássicos de Bühlmann-Straub e Jewell, posteriormente debruçamo-nos sobre a mais recente leitura destes modelos enquanto modelos lineares mistos. Por fim, apresentamos duas abordagens que sugerem como a credibilidade de Bühlmann poderá aperfeiçoar o desempenho dos modelos lineares generalizados.
In our report, several interpretations of Bühlmann credibility are applied in the workers compensation portfolio of a portuguese insurance company. We begin with classical implementations of Bühlmann-Straub and Jewell models, and then we display a more recent reading of those models as Linear Mixed Models. We end presenting two approaches that show how Bühlmann credibility can enhance the performance of generalized linear models.
Lee, Min Cherng. "Multiple imputation for missing data and statistical disclosure control for mixed-mode data using a sequence of generalised linear models." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/366481/.
Повний текст джерелаWang, Yu. "A study on the type I error rate and power for generalized linear mixed model containing one random effect." Kansas State University, 2017. http://hdl.handle.net/2097/35301.
Повний текст джерелаDepartment of Statistics
Christopher Vahl
In animal health research, it is quite common for a clinical trial to be designed to demonstrate the efficacy of a new drug where a binary response variable is measured on an individual experimental animal (i.e., the observational unit). However, the investigational treatments are applied to groups of animals instead of an individual animal. This means the experimental unit is the group of animals and the response variable could be modeled with the binomial distribution. Also, the responses of animals within the same experimental unit may then be statistically dependent on each other. The usual logit model for a binary response assumes that all observations are independent. In this report, a logit model with a random error term representing the group of animals is considered. This is model belongs to a class of models referred to as generalized linear mixed models and is commonly fit using the SAS System procedure PROC GLIMMIX. Furthermore, practitioners often adjust the denominator degrees of freedom of the test statistic produced by PROC GLIMMIX using one of several different methods. In this report, a simulation study was performed over a variety of different parameter settings to compare the effects on the type I error rate and power of two methods for adjusting the denominator degrees of freedom, namely “DDFM = KENWARDROGER” and “DDFM = NONE”. Despite its reputation for fine performance in linear mixed models with normally distributed errors, the “DDFM = KENWARDROGER” option tended to perform poorly more often than the “DDFM = NONE” option in the logistic regression model with one random effect.
Costa, Silvano Cesar da. "Modelos lineares generalizados mistos para dados longitudinais." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-09052003-164143/.
Повний текст джерелаExperiments which response variables are proportions or counts are very common in several research areas, specially in the area of agriculture. The theory of generalized linear models, well difused (McCullagh & Nelder, 1989; Demetrio, 2001), is used for analyzing these experiments where the responses are independent. If the estimated variance is greater than the expected variance, the dispersion parameter is estimated including it on the parameter estimation process. When the response variable is observed over time a correlation among observations might occur and it should be taken into account in the parameter estimation. A way of dealing with this correlation is applying the methodology of generalized estimating equations (GEEs) discussed by Liang & Zeger (1986) although, in this case, the interest is on the estimates of the xed efect being the inclusion of a working correlation matrix useful to obtain more accurate estimates. Another alternative is the inclusion of a latent efect in the linear predictor to explain variabilities not considered in the model that might in uence the results. In this work the random efect and the dispersion parameter are combined and included together in the parameter estimation. Such methodology is applied to a data set obtained from an experiment realized with camu-camu to evaluate, through proportion of grafting well successful of seedling, which kind of grafting and understock are suitable to be used. Several models are fitted, since the split plot model (with independence assumption) up to the model where the dispersion parameter and the random efect are considered together. There is evidence that the model including the random efect and the dispersion parameter together, produce better estimates of the parameters. Another longitudinal data set used here comes from an experiment realized with the MON810 transgenic corn where the response variable is the number of caterpillars (Spodoptera frugiperda). In this case, due to the excessive number of zeros obtained, the zero in ated Poisson regression model (ZIP) is used in addition to the standard Poisson model, where observations are considered independent, and the zero in ated Poisson regression model with random efect. The results show that the random efect included in the linear predictor was not significant and, therefore, the adopted model is the zero in ated Poisson regression model. The results were obtained using the procedures NLMIXED, GENMOD and GPLOT available on SAS - Statistical Analysis System, version 8.2.
Bautista, Ezequiel Abraham López. "Modelos lineares mistos e generalizados mistos em estudos de adaptação local e plasticidade fenotípica de Euterpe edulis." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-11092014-170903/.
Повний текст джерелаThe aim of this work was to evaluate the presence of phenotypic plasticity and local adaptation of three provenances of the palm specie Euterpe edulis: Atlantic Rainforest, Seasonally Dry Forest and Restinga Forest, in permanent parcels inserted in three forest types of the São Paulo State (Brazil): Parque Estadual da Ilha do Cardoso, Parque Estadual de Carlos Botelho e Estação Ecológica dos Caetetus, in experiments of seedling establishment and juveniles plants growth. The data sets were analyzed using structures of groups of experiments, with crossed and nested effects. The variables related to dry matter content of plants in both assays were analyzed using linear mixed models (LMM) approach, through the incorporation of random effect factors, and using the restricted maximum likelihood method (REML) for estimation of variance components associated with these factors with a minor bias. On the other hand, germination proportion of the seeds at seedling establishment assay was analyzed using the generalized linear mixed models (GLMM) approach, under the assumption that the variable follows a binomial distribution, with logit link function. The pseudo-likelihood (PL) method was used to obtain the numerical solution of the likelihood equations. The results showed that, plants from seeds of the three biomes evaluated presented a plastic behavior for all characters assessed in the seedling establishment assay. In respect to juveniles adaptation assay, the phenotypic plasticity characteristic was observed only to the leaf dry matter content of plants from Seasonally Dry Forest biome. The local adaptation characteristic was clearly observed in the seedling establishment assay. These results showed that at each site evaluated, plants originating from seeds of different provenances exhibited different behavior on characters related to the dry matter content and may in some cases be local adaptation. It was concluded that locations Carlos Botelho and Ilha do Cardoso are the most favorable for seed germination of its same provenance.
Eldridge, James Vincent. "Landscape ecology of the lesser grain borer, Rhyzopertha dominica." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/69538/2/James_Eldridge_Thesis.pdf.
Повний текст джерелаRodríguez, Sanz Maica 1974. "Evolución de las desigualdades socioeconómicas en la mortalidad prematura en los barrios de Barcelona." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/664848.
Повний текст джерелаThe objective is to analyze trends in socioeconomic inequalities in mortality in the neighborhoods of Barcelona, taking into account the population changes. We have three studies. A review of the use of area-level socioeconomic indicators in epidemiological research, in Spain, and its association with health and health inequalities. An analysis of twenty years of trends in socioeconomic inequalities in premature mortality in the neighborhoods of Barcelona, accounting for immigration in neighborhoods. An analysis of ten years of trends in socioeconomic inequalities in premature mortality in the neighborhoods of Barcelona, in foreign-born and native population. In Barcelona, socioeconomic inequalities between neighborhoods persist, there is an excess of premature mortality in the most disadvantaged neighborhoods. Last years, these inequalities tend to diminish, related to the arrival of immigrant population. Foreign-born population register lower levels of premature mortality than native population, and without inequalities between neighborhoods.
Baker, Jannah F. "Bayesian spatiotemporal modelling of chronic disease outcomes." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104455/1/Jannah_Baker_Thesis.pdf.
Повний текст джерелаShen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.
Повний текст джерелаSabangan, Rainier Monteclaro. "Identification and Estimation of Location and Dispersion Effects in Unreplicated 2k-p Designs Using Generalized Linear Models." Bowling Green State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1269014397.
Повний текст джерелаLetsoalo, Marothi Peter. "Assessing variance components of multilevel models pregnancy data." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2873.
Повний текст джерелаMost social and health science data are longitudinal and additionally multilevel in nature, which means that response data are grouped by attributes of some cluster. Ignoring the differences and similarities generated by these clusters results to misleading estimates, hence motivating for a need to assess variance components (VCs) using multilevel models (MLMs) or generalised linear mixed models (GLMMs). This study has explored and fitted teenage pregnancy census data that were gathered from 2011 to 2015 by the Africa Centre at Kwa-Zulu Natal, South Africa. The exploration of these data revealed a two level pure hierarchy data structure of teenage pregnancy status for some years nested within female teenagers. To fit these data, the effects that census year (year) and three female characteristics (namely age (age), number of household membership (idhhms), number of children before observation year (nch) have on teenage pregnancy were examined. Model building of this work, firstly, fitted a logit gen eralised linear model (GLM) under the assumption that teenage pregnancy measurements are independent between females and secondly, fitted a GLMM or MLM of female random effect. A better fit GLMM indicated, for an additional year on year, a 0.203 decrease on the log odds of teenage pregnancy while GLM suggested a 0.21 decrease and 0.557 increase for each additional year on age and year, respectively. A GLM with only year effect uncovered a fixed estimate which is higher, by 0.04, than that of a better fit GLMM. The inconsistency in the effect of year was caused by a significant female cluster variance of approximately 0.35 that was used to compute the VCs. Given the effect of year, the VCs suggested that 9.5% of the differences in teenage pregnancy lies between females while 0.095 similarities (scale from 0 to 1) are for the same female. It was also revealed that year does not vary within females. Apart from the small differences between observed estimates of the fitted GLM and GLMM, this work produced evidence that accounting for cluster effect improves accuracy of estimates. Keywords: Multilevel Model, Generalised Linear Mixed Model, Variance Components, Hier archical Data Structure, Social Science Data, Teenage Pregnancy
Johnson, Nels Gordon. "Semiparametric Regression Methods with Covariate Measurement Error." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/49551.
Повний текст джерелаThe first model is the matched case-control study for analyzing clustered binary outcomes. We develop low-rank thin plate splines for the case where a variable measured with error has an unknown, nonlinear relationship with the response. In addition to the semi- and fully Bayesian approaches, we propose another using expectation-maximization to detect both parametric and nonparametric relationships between the covariates and the binary outcome. We assess the performance of each method via simulation terms of mean squared error and mean bias. We illustrate each method on a perturbed example of 1--4 matched case-control study.
The second regression model is the generalized linear model (GLM) with unknown link function. Usually, the link function is chosen by the user based on the distribution of the response variable, often to be the canonical link. However, when covariates are measured with error, incorrect inference as a result of the error can be compounded by incorrect choice of link function. We assess performance via simulation of the semi- and fully Bayesian methods in terms of mean squared error. We illustrate each method on the Framingham Heart Study dataset.
The simulation results for both regression models support that the fully Bayesian approach is at least as good as the semi-Bayesian approach for adjusting for measurement error, particularly when the distribution of the variable of measure with error and the distribution of the measurement error are misspecified.
Ph. D.
Carrico, Robert. "Unbiased Estimation for the Contextual Effect of Duration of Adolescent Height Growth on Adulthood Obesity and Health Outcomes via Hierarchical Linear and Nonlinear Models." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2817.
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