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Dissertations / Theses on the topic 'Generalised linear model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

Shrewsbury, 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.

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Generalised linear models (GLMs) provide a flexible and sound basis for calibrating gravity models for trip distribution, for a wide range of deterrence functions (from steps to splines), with K factors and geographic segmentation. The Tanner function fitted Wellington Transport Strategy Model data as well as more complex functions and was insensitive to the formulation of intrazonal and external costs. Weighting from variable expansion factors and interpretation of the deviance under sparsity are addressed. An observed trip matrix is disaggregated and fitted at the household, person and trip

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3

YAN, HONGXUAN. "Generalised linear Gegenbauer long memory models for time series of counts with financial and insurance applications." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19660.

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The contribution of this thesis is on developing new models and applying them to analyse data in finance and insurance. The proposed generalised linear Gegenbauer autoregressive moving average models (GLGARMA) incorporate GARMA into the mean functions of the four different count distributions within a GLM framework under the parameter-driven and observation-driven approaches. Model properties in the time domain and spectral density function in the frequency domain are studied and compared to seasonal long memory model. The approximated spectral density function of the PD GLGARMA model is deriv

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4

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.

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The Lesser Grain Borer is a major pest of stored grain with a global distribution. This project has, for the first time recorded this pest throughout broad spatial areas, tens of kilometres from grain production or storage. Statistical analysis revealed that different factors such as ambient temperature and the availability of food resources affect R. dominica differently between different habitats. This suggests that, contrary to the prevailing view, this pest is not solely dependent on stored wheat and can continue to persist throughout a range of habitats. These findings have important mana

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5

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.

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This dissertation develops influence diagnostics for crossover models. Mixed linear models and generalised mixed linear models are utilised to investigate continuous and count data from crossover studies, respectively. For both types of models, changes in the maximum likelihood estimates of parameters, particularly in the estimated treatment effect, due to minor perturbations of the observed data, are assessed. The novelty of this dissertation lies in the analytical derivation of influence diagnostics using decompositions of the perturbed mixed models. Consequently, the suggested influence dia

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6

Qiao, Zhen. "Assessment of the mortality displacement in temperature-related deaths in Brisbane, Australia." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76280/1/Zhen_Qiao_Thesis.pdf.

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This thesis is a population-based ecological study designed to investigate the issue of mortality displacement (or "harvesting" effect) in the assessment of temperature-related deaths in Brisbane, Australia. It examines the temperature impacts on mortality, and assesses the harvesting effects on the temperature–related deaths. This study contributes to the knowledge base of understanding the temperature-mortality relationship and assists in formulating and evaluating public health intervention strategies within the context of climate change.

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7

Hu, 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.

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This thesis mainly concerns the statistical modelling and machine learning methods for the analysis of longitudinal data. As a contribution to this area, this thesis provides theoretical discussion and empirical illustrations of longitudinal data analysis. The first contribution is developing methods to obtain robust and efficient variance estimators when the cluster size is large. The second one is comparing a traditional parametric approach, the linear mixed model with machine learning methods in longitudinal data analysis. The last one is extracting new features to improve sheep behaviour c

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8

Letsoalo, Marothi Peter. "Assessing variance components of multilevel models pregnancy data." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2873.

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Thesis (M. Sc. (Statistics)<br>Most 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 exp

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9

Carstens, Herman. "A Bayesian approach to energy monitoring optimization." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/63791.

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This thesis develops methods for reducing energy Measurement and Verification (M&V) costs through the use of Bayesian statistics. M&V quantifies the savings of energy efficiency and demand side projects by comparing the energy use in a given period to what that use would have been, had no interventions taken place. The case of a large-scale lighting retrofit study, where incandescent lamps are replaced by Compact Fluorescent Lamps (CFLs), is considered. These projects often need to be monitored over a number of years with a predetermined level of statistical rigour, making M&V very expen

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10

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.

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This thesis contributes to Bayesian spatial and spatiotemporal methodology by investigating techniques for spatial imputation and joint disease modelling, and identifies high-risk individual profiles and geographic areas for type II diabetes mellitus (DMII) outcomes. DMII and related chronic conditions including hypertension, coronary arterial disease, congestive heart failure and chronic obstructive pulmonary disease are examples of ambulatory care sensitive conditions for which hospitalisation for complications is potentially avoidable with quality primary care. Bayesian spatial and spatiote

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11

Sammut, Fiona. "Using generalized linear models to model compositional response data." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89876/.

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This work proposes a multivariate logit model which models the influence of explanatory variables on continuous compositional response variables. This multivariate logit model generalizes an elegant method that was suggested previously by Wedderburn (1974) for the analysis of leaf blotch data in the special case of J = 2, leading to our naming this new approach as the generalized Wedderburn method. In contrast to the logratio modelling approach devised by Aitchison (1982, J. Roy Stat. Soc. B.), the multivariate logit model used under the generalized Wedderburn approach models the expectation o

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12

Carlos, Monteiro Ponce de Leon Antonio. "Optimum experimental design for model discrimination and generalized linear models." Thesis, London School of Economics and Political Science (University of London), 1993. http://etheses.lse.ac.uk/2434/.

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The main subject of this thesis concerns the optimum design of experiments for discriminating between two rival mathematical models. In addition, optimality of designs for parameter estimation is investigated although restricted to binary response models. Optimal design theory and generalized linear models form the background for this work. The former provides the tools for construction of the optimum designs whereas the latter provides the framework in which the methods are developed. For model discrimination the procedures which are proposed may not only be applied to compare two regression

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13

Zulj, Valentin. "On The Jackknife Averaging of Generalized Linear Models." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412831.

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Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-v

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14

Greenaway, Mark Jonathan. "Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20233.

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Approximate Bayesian inference methods offer methodology for fitting Bayesian models as fast alternatives to Markov Chain Monte Carlo methods that sometimes have only a slight loss of accuracy. In this thesis, we consider variable selection for linear models, and zero inflated mixed models. Variable selection for linear regression models are ubiquitous in applied statistics. We use the popular g-prior (Zellner, 1986) for model selection of linear models with normal priors where g is a prior hyperparameter. We derive exact expressions for the model selection Bayes Factors in terms of special f

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15

Vasconcelos, Julio Cezar Souza. "Modelo linear parcial generalizado simétrico." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-26072017-105153/.

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Neste trabalho foi proposto o modelo linear parcial generalizado simétrico, com base nos modelos lineares parciais generalizados e nos modelos lineares simétricos, em que a variável resposta segue uma distribuição que pertence à família de distribuições simétricas, considerando um preditor linear que possui uma parte paramétrica e uma não paramétrica. Algumas distribuições que pertencem a essa classe são as distribuições: Normal, t-Student, Exponencial potência, Slash e Hiperbólica, dentre outras. Uma breve revisão dos conceitos utilizados ao longo do trabalho foram apresentados, a saber: anál

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16

Holmberg, Henrik. "Generalised linear models with clustered data." Licentiate thesis, Umeå universitet, Statistiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39972.

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In situations where a large data set is partitioned into many relativelysmall clusters, and where the members within a cluster have some common unmeasured characteristics, the number of parameters requiring estimation tends to increase with sample size if a fixed effects model is applied. This fact causes the assumptions underlying asymptotic results to be violated. The first paper in this thesis considers two possible solutions to this problem, a random intercepts model and a fixed effects model, where asymptoticsare replaced by a simple form of bootstrapping. A profiling approach is introduc

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17

Scallan, Anthony. "Some applications of generalised linear models." Thesis, London Metropolitan University, 1990. http://repository.londonmet.ac.uk/2958/.

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This thesis is concerned with some extensions to and applications of generalised linear models and their implementation in a statistical package. The principal extension considered is the inclusion of extra parameters in the link function of the model in order to create a family of parametric link functions. This technique is applied to standard link functions as well as to the family of composite link functions. The applications of such models are illustrated by reference to several examples. The techniques presented enable complicated models to be fitted in a unified and consistent manner, w

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18

Mackinnon, Murray J. "Collinearity in generalized linear models." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/25711.

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The concept of collinearity for generalized linear models is introduced and compared to that for standard linear models. Two approaches for detecting collinearity are presented and shown to lead to the same diagnostic procedure. These are analysed for the Poisson, gamma, inverse Gaussian, pth order, binomial proportion and negative binomial models. A bound is derived for the degree of collinearity in a generalized linear model in terms of that of the standard linear model. Estimation methods based on ridge, prior likelihood and principal components are proposed, and briefly illustrated with a

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19

Benghiat, Sonia. "Diagnostics for generalized linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ64046.pdf.

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20

Creagh-Osborne, Jane. "Latent variable generalized linear models." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1885.

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Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed effect models where response data arise from exponential family distributions. Much recent research has attempted to extend the framework to include random effects in the linear predictors. Different methodologies have been employed to solve different motivating problems, for example Generalized Linear Mixed Models (Clayton, 1994) and Multilevel Models (Goldstein, 1995). A thorough review and classification of this and related material is presented. In Item Response Theory (IRT) subjects are tes

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21

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

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22

Rusch, Thomas, and Achim Zeileis. "Gaining Insight with Recursive Partitioning of Generalized Linear Models." Taylor and Francis, 2013. http://dx.doi.org/10.1080/00949655.2012.658804.

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Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearly give rise to different model parameter values. We present an approach that combines generalized linear models with recursive partitioning that offers en

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23

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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24

Ten, Eyck Patrick. "Problems in generalized linear model selection and predictive evaluation for binary outcomes." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/6003.

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This manuscript consists of three papers which formulate novel generalized linear model methodologies. In Chapter 1, we introduce a variant of the traditional concordance statistic that is associated with logistic regression. This adjusted c − statistic as we call it utilizes the differences in predicted probabilities as weights for each event/non- event observation pair. We highlight an extensive comparison of the adjusted and traditional c-statistics using simulations and apply these measures in a modeling application. In Chapter 2, we feature the development and investigation of three model

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25

Rusch, Thomas, and Achim Zeileis. "Gaining Insight With Recursive Partitioning Of Generalized Linear Models." WU Vienna University of Economics and Business, 2011. http://epub.wu.ac.at/3143/6/paperEPUB.pdf.

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Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearily give rise to different model parameter values. We present an approach that combines generalized linear models with recursive partitioning th

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26

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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27

Zhao, Chen. "Evaluating Health Policy Effect with Generalized Linear Model and Generalized Estimating Equation Model." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586377218891854.

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28

Mayo-Bruinsma, Liesha. "Family-centered Care Delivery: Comparing Models of Primary Care Service Delivery in Ontario." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19952.

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Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ sig

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29

Valente, Ana Beatriz Marques Cabral. "Health insurance pricing with generalised linear models." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20988.

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Mestrado em Actuarial Science<br>Os Modelos Lineares Generalizados (GLMs) são amplamente utilizados na precificação de seguros do ramo Não Vida. O prémio cobrado pela seguradora é calculado com base em uma tarifa. A abordagem clássica para estimar o prémio é feita assumindo a independência entre o número de sinistros e o seu custo. A partir desta independência, a frequência e a severidade dos sinistros são estimados através de GLMs separados e a tarifa é obtida combinando os dois modelos. O presente relatório fornece uma breve introdução sobre a metodologia e descreve como preparámos os dados

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30

Emond, Mary Jane. "Efficient estimation in the generalized semilinear model /." Thesis, Connect to this title online; UW restricted, 1993. http://hdl.handle.net/1773/9543.

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31

Stroinski, Krzysztof Jerzy. "Generalized linear models in motor insurance." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1044.

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32

Holmberg, Henrik. "Generalized linear models with clustered data." Doctoral thesis, Umeå universitet, Statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-52902.

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In situations where a large data set is partitioned into many relatively small groups, and where the members within a group have some common unmeasured characteristics, the number of parameters requiring estimation tends to increase with sample size if a fixed effects model is applied. This fact causes the assumptions underlying asymptotic results to be violated. The first paper in this thesis considers two possible solutions to this problem, a random intercepts model and a fixed effects model, where asymptotics are replaced by a simple form of bootstrapping. A profiling approach is introduced

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33

Jiang, Dingfeng. "Concave selection in generalized linear models." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2902.

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A family of concave penalties, including the smoothly clipped absolute deviation (SCAD) and minimax concave penalties (MCP), has been shown to have attractive properties in variable selection. The computation of concave penalized solutions, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm to compute the solutions of concave penalized generalized linear models (GLM). In contrast to the existing algorithms that uses local quadratic or local linear approximation of the penalty, the MMCD majo

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34

Johannes, Jan. "Verallgemeinerte Maximum-Likelihood-Methoden und der selbstinformative Grenzwert." Doctoral thesis, [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=96644258X.

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35

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 t

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36

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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37

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 th

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38

Li, Zuojing. "Longitudinal data analysis using generalized linear models." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27267.

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In this work we examine various conditions under which the usual asymptotic results (i.e. the weak consistency, the asymptotic normality and the strong consistency) hold for the regressor parameter beta which arises in a linear model (Chapter 2), a generalized linea model (GLM) with a fully specified likelihood (Chapter 3) or as a root of the generalized estimating equation (GEE) associated with a sequence of longitudinal observations (Chapter 4). Our main references for each of these chapters are [12], [9], respectively [20]. We provide detailed proofs of the results found in the above-mentio

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39

Hamzah, Nor Aishah. "Robust regression estimation in generalized linear models." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294372.

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Byrne, Evan. "Inference in Generalized Linear Models with Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555152640361367.

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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.<br>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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Barbosa, Luciano [UNESP]. "Metodologias estatísticas na análise de germinação de sementes de mamona." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/101848.

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Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-11-16Bitstream added on 2014-06-13T21:02:57Z : No. of bitstreams: 1 barbosa_l_dr_botfca.pdf: 2587351 bytes, checksum: 76e343f1e0edbbbee5cb996188d8efd2 (MD5)<br>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 se

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Utami, Zuliana Sri. "Penalized regression methods with application to generalized linear models, generalized additive models, and smoothing." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20908/.

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Recently, penalized regression has been used for dealing problems which found in maximum likelihood estimation such as correlated parameters and a large number of predictors. The main issues in this regression is how to select the optimal model. In this thesis, Schall’s algorithm is proposed as an automatic selection of weight of penalty. The algorithm has two steps. First, the coefficient estimates are obtained with an arbitrary penalty weight. Second, an estimate of penalty weight λ can be calculated by the ratio of the variance of error and the variance of coefficient. The iteration is cont

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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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Dunn, Peter Kenneth. "Likelihood-based inference for tweedie generalized linear models /." St. Lucia, Qld, 2001. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16472.pdf.

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Waterhouse, Timothy Hugh. "Optimal experiemental design for nonlinear and generalised linear models /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18919.pdf.

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47

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 a

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Santana, Tiago Viana Flor de. "Modelo linear beta Weibull generalizado: propriedades, estimação, diagnóstico e aplicações." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-29112016-150708/.

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Neste trabalho dois novos modelos estatísticos de regressão são propostos, com estrutura muito semelhante aos Modelos Lineares Generalizados (MLG) porém, admitindo as distribuições Weibull exponenciada (WE) e beta Weibull (BW) para o componente aleatório as quais não pertencem a família exponencial como é requerido em MLG. Os novos modelos trazem uma nova abordagem para as distribuições admitidas em modelos de regressão e estende o MLG para além da família exponencial. Os modelos, nomeados por Modelo Linear Weibull Exponeciada Generalizado (MLWEG) e Modelo Linear Beta Weibull Generalizado (MLB

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49

Forrester, Marie Leanne. "Epidemic models and inference for the transmission of hospital pathogens." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16419/1/Marie_Forrester_Thesis.pdf.

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Abstract:

The primary objective of this dissertation is to utilise, adapt and extend current stochastic models and statistical inference techniques to describe the transmission of nosocomial pathogens, i.e. hospital-acquired pathogens, and multiply-resistant organisms within the hospital setting. The emergence of higher levels of antibiotic resistance is threatening the long term viability of current treatment options and placing greater emphasis on the use of infection control procedures. The relative importance and value of various infection control practices is often debated and there is a lack of qu

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50

Forrester, Marie Leanne. "Epidemic models and inference for the transmission of hospital pathogens." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16419/.

Full text

Abstract:

The primary objective of this dissertation is to utilise, adapt and extend current stochastic models and statistical inference techniques to describe the transmission of nosocomial pathogens, i.e. hospital-acquired pathogens, and multiply-resistant organisms within the hospital setting. The emergence of higher levels of antibiotic resistance is threatening the long term viability of current treatment options and placing greater emphasis on the use of infection control procedures. The relative importance and value of various infection control practices is often debated and there is a lack of qu

APA, Harvard, Vancouver, ISO, and other styles

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