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Moustaki, Irini. "Latent variable models for mixed manifest variables". Thesis, London School of Economics and Political Science (University of London), 1996. http://etheses.lse.ac.uk/78/.
Pełny tekst źródłaChang, Soong Uk. "Clustering with mixed variables /". [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.
Pełny tekst źródłaMahat, Nor Idayu. "Some investigations in discriminant analysis with mixed variables". Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432783.
Pełny tekst źródłaPelamatti, Julien. "Mixed-variable Bayesian optimization : application to aerospace system design". Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I003.
Pełny tekst źródłaWithin the framework of complex system design, such as aircraft and launch vehicles, the presence of computationallyintensive objective and/or constraint functions (e.g., finite element models and multidisciplinary analyses)coupled with the dependence on discrete and unordered technological design choices results in challenging optimizationproblems. Furthermore, part of these technological choices is associated to a number of specific continuous anddiscrete design variables which must be taken into consideration only if specific technological and/or architecturalchoices are made. As a result, the optimization problem which must be solved in order to determine the optimalsystem design presents a dynamically varying search space and feasibility domain.The few existing algorithms which allow solving this particular type of problems tend to require a large amountof function evaluations in order to converge to the feasible optimum, and result therefore inadequate when dealingwith the computationally intensive problems which can often be encountered within the design of complex systems.For this reason, this thesis explores the possibility of performing constrained mixed-variable and variable-size designspace optimization by relying on surrogate model-based design optimization performed with the help of Gaussianprocesses, also known as Bayesian optimization. More specifically, 3 main axes are discussed. First, the Gaussianprocess surrogate modeling of mixed continuous/discrete functions and the associated challenges are extensivelydiscussed. A unifying formalism is proposed in order to facilitate the description and comparison between theexisting kernels allowing to adapt Gaussian processes to the presence of discrete unordered variables. Furthermore,the actual modeling performances of these various kernels are tested and compared on a set of analytical and designrelated benchmarks with different characteristics and parameterizations.In the second part of the thesis, the possibility of extending the mixed continuous/discrete surrogate modeling toa context of Bayesian optimization is discussed. The theoretical feasibility of said extension in terms of objective/-constraint function modeling as well as acquisition function definition and optimization is shown. Different possiblealternatives are considered and described. Finally, the performance of the proposed optimization algorithm, withvarious kernels parameterizations and different initializations, is tested on a number of analytical and design relatedtest-cases and compared to reference algorithms.In the last part of this manuscript, two alternative ways of adapting the previously discussed mixed continuous/discrete Bayesian optimization algorithms in order to solve variable-size design space problems (i.e., problemscharacterized by a dynamically varying design space) are proposed. The first adaptation is based on the paralleloptimization of several sub-problems coupled with a computational budget allocation based on the informationprovided by the surrogate models. The second adaptation, instead, is based on the definition of a kernel allowingto compute the covariance between samples belonging to partially different search spaces based on the hierarchicalgrouping of design variables. Finally, the two alternatives are tested and compared on a set of analytical and designrelated benchmarks.Overall, it is shown that the proposed optimization methods allow to converge to the various constrained problemoptimum neighborhoods considerably faster when compared to the reference methods, thus representing apromising tool for the design of complex systems
Lazare, Arnaud. "Global optimization of polynomial programs with mixed-integer variables". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLY011.
Pełny tekst źródłaIn this thesis, we are interested in the study of polynomial programs, that is optimization problems for which the objective function and/or the constraints are expressed by multivariate polynomials. These problems have many practical applications and are currently actively studied. Different methods can be used to find either a global or a heuristic solution, using for instance, positive semi-definite relaxations as in the "Moment/Sum of squares" method. But these problems remain very difficult and only small instances are addressed. In the quadratic case, an effective exact solution approach was initially proposed in the QCR method. It is based on a quadratic convex reformulation, which is optimal in terms of continuous relaxation bound.One of the motivations of this thesis is to generalize this approach to the case of polynomial programs. In most of this manuscript, we study optimization problems with binary variables. We propose two families of convex reformulations for these problems: "direct" reformulations and quadratic ones.For direct reformulations, we first focus on linearizations. We introduce the concept of q-linearization, that is a linearization using q additional variables, and we compare the bounds obtained by continuous relaxation for different values of q. Then, we apply convex reformulation to the polynomial problem, by adding additional terms to the objective function, but without adding additional variables or constraints.The second family of convex reformulations aims at extending quadratic convex reformulation to the polynomial case. We propose several new alternative reformulations that we compare to existing methods on instances of the literature. In particular we present the algorithm PQCR to solve unconstrained binary polynomial problems. The PQCR method is able to solve several unsolved instances. In addition to numerical experiments, we also propose a theoretical study to compare the different quadratic reformulations of the literature and then apply a convex reformulation to them.Finally, we consider more general problems and we propose a method to compute convex relaxations for continuous problems
Bonnet, Anna. "Heritability Estimation in High-dimensional Mixed Models : Theory and Applications". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS498/document.
Pełny tekst źródłaWe study statistical methods toestimate the heritability of a biological trait,which is the proportion of variations of thistrait that can be explained by genetic factors.First, we propose to study the heritability ofquantitative traits using high-dimensionalsparse linear mixed models. We investigate thetheoretical properties of the maximumlikelihood estimator for the heritability and weshow that it is a consistent estimator and that itsatisfies a central limit theorem with a closedformexpression for the asymptotic variance.This result, supported by an extendednumerical study, shows that the variance of ourestimator is strongly affected by the ratiobetween the number of observations and thesize of the random genetic effects. Moreprecisely, when the number of observations issmall compared to the size of the geneticeffects (which is often the case in geneticstudies), the variance of our estimator is verylarge. This motivated the development of avariable selection method in order to capturethe genetic variants which are involved themost in the phenotypic variations and providemore accurate heritability estimations. Wepropose then a variable selection methodadapted to high dimensional settings and weshow that, depending on the number of geneticvariants actually involved in the phenotypicvariations, called causal variants, it was a goodidea to include or not a variable selection stepbefore estimating heritability.The last part of this thesis is dedicated toheritability estimation for binary data, in orderto study the proportion of genetic factorsinvolved in complex diseases. We propose tostudy the theoretical properties of the methoddeveloped by Golan et al. (2014) for casecontroldata, which is very efficient in practice.Our main result is the proof of the consistencyof their heritability estimator
Adamec, Vaclav. "The Effect of Maternal and Fetal Inbreeding on Dystocia, Calf Survival, Days to First Service and Non-Return Performance in U.S. Dairy Cattle". Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/25999.
Pełny tekst źródłaPh. D.
Fernández, Villegas Renzo. "A beta inflated mean regression model with mixed effects for fractional response variables". Master's thesis, Pontificia Universidad Católica del Perú, 2017. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8847.
Pełny tekst źródłaEn este artículo proponemos un nuevo modelo de regresión con efectos mixtos para variables acotadas fraccionarias. Este modelo nos permite incorporar covariables directamente al valor esperado, de manera que podemos cuantificar exactamente la influencia de estas covariables en la media de la variable de interés en vez de en la media condicional. La estimación se llevó a cabo desde una perspectiva bayesiana y debido a la complejidad de la distribución aumentada a posteriori usamos un algoritmo de Monte Carlo Hamiltoniano, el muestreador No-U-Turn, que se encuentra implementado en el software Stan. Se realizó un estudio de simulación que compara, en términos de sesgo y RMSE, el modelo propuesto con otros modelos tradicionales longitudinales para variables acotadas, resultando que el primero tiene un mejor desempeño. Finalmente, aplicamos nuestro modelo de regresión Beta Inflacionada con efectos mixtos a datos reales los cuales consistían en información de la utilización de las líneas de crédito en el sistema financiero peruano.
Tesis
Dahito, Marie-Ange. "Constrained mixed-variable blackbox optimization with applications in the automotive industry". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS017.
Pełny tekst źródłaNumerous industrial optimization problems are concerned with complex systems and have no explicit analytical formulation, that is they are blackbox optimization problems. They may be mixed, namely involve different types of variables (continuous and discrete), and comprise many constraints that must be satisfied. In addition, the objective and constraint blackbox functions may be computationally expensive to evaluate.In this thesis, we investigate solution methods for such challenging problems, i.e constrained mixed-variable blackbox optimization problems involving computationally expensive functions.As the use of derivatives is impractical, problems of this form are commonly tackled using derivative-free approaches such as evolutionary algorithms, direct search and surrogate-based methods.We investigate the performance of such deterministic and stochastic methods in the context of blackbox optimization, including a finite element test case designed for our research purposes. In particular, the performance of the ORTHOMADS instantiation of the direct search MADS algorithm is analyzed on continuous and mixed-integer optimization problems from the literature.We also propose a new blackbox optimization algorithm, called BOA, based on surrogate approximations. It proceeds in two phases, the first of which focuses on finding a feasible solution, while the second one iteratively improves the objective value of the best feasible solution found. Experiments on instances stemming from the literature and applications from the automotive industry are reported. They namely include results of our algorithm considering different types of surrogates and comparisons with ORTHOMADS
Mohd, Isa Khadijah. "Corporate taxpayers’ compliance variables under the self-assessment system in Malaysia : a mixed methods approach". Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1796.
Pełny tekst źródłaJia, Yanan Jia. "Generalized Bilinear Mixed-Effects Models for Multi-Indexed Multivariate Data". The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469180629.
Pełny tekst źródłaArendt, Christopher D. "Adaptive Pareto Set Estimation for Stochastic Mixed Variable Design Problems". Ft. Belvoir : Defense Technical Information Center, 2009. http://handle.dtic.mil/100.2/ADA499860.
Pełny tekst źródłaBarjhoux, Pierre-Jean. "Towards efficient solutions for large scale structural optimization problems with categorical and continuous mixed design variables". Thesis, Toulouse, ISAE, 2020. http://depozit.isae.fr/theses/2020/2020_Barjhoux_Pierre-Jean.pdf.
Pełny tekst źródłaNowadays in the aircraft industry, structural optimization problemscan be really complex and combine changes in choices of materials, stiffeners, orsizes/types of elements. In this work, it is proposed to solve large scale structural weightminimization problems with both categorical and continuous variables, subject to stressand displacements constraints. Three algorithms have been proposed. As a first attempt,an algorithm based on the branch and bound generic framework has been implemented.A specific formulation to compute lower bounds has been proposed. According to thenumerical tests, the algorithm returned the exact optima. However, the exponentialscalability of the computational cost with respect to the number of structural elementsprevents from an industrial application. The second algorithm relies on a bi-level formulationof the mixed categorical problem. The master full categorical problem consists ofminimizing a first order like approximation of the slave problem with respect to the categoricaldesign variables. The method offers a quasi-linear scaling of the computationalcost with respect to the number of elements and categorical values. Finally, in the thirdapproach the optimization problem is formulated as a bi-level mixed integer non-linearprogram with relaxable design variables. Numerical tests include an optimization casewith more than one hundred structural elements. Also, the computational cost scalingis quasi-independent from the number of available categorical values per element
Weld, Christopher. "Computational Graphics and Statistical Analysis: Mixed Type Random Variables, Confidence Regions, and Golden Quantile Rank Sets". W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1563898977.
Pełny tekst źródłaMuazu, Naseer Babangida. "Comparative analysis of domestic fuel-wood energy consumption between South Africa and Nigeria: A mixed methods approach". University of Western Cape, 2019. http://hdl.handle.net/11394/7473.
Pełny tekst źródłaSouth Africa was considered to have attained universal access to modern energy, this meant that the number of households that have access to energy had successfully increased from 30% in 1994 to 87% in 2012. However, the situation in Nigeria is such that electricity generating figures are very poor and they cannot meet half of the demand of Nigerian households, and the majority of the states have challenges in accessing sufficient fossil fuels. However, recent trends in domestic energy consumption for both countries are becoming biased in favor of fuel-wood energy especially among low-income households, “descending the energy ladder”.
Leone, Suzanna. "The Relationship between Classroom Climate Variables and Student Achievement". Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1256594309.
Pełny tekst źródłaTʻang, Min. "Extention of evaluating the operating characteristics for dependent mixed variables-attributes sampling plans to large first sample size /". Online version of thesis, 1991. http://hdl.handle.net/1850/11208.
Pełny tekst źródłaLata, Mary Elizabeth. "Variables affecting first order fire effects, characteristics, and behavior in experimental and prescribed fires in mixed and tallgrass prairie". Diss., University of Iowa, 2006. http://ir.uiowa.edu/etd/72.
Pełny tekst źródłaYe, Xin. "Development of models for understanding causal relationships among activity and travel variables". [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001842.
Pełny tekst źródłaDion, Charlotte. "Estimation non-paramétrique de la densité de variables aléatoires cachées". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM031/document.
Pełny tekst źródłaThis thesis contains several nonparametric estimation procedures of a probability density function.In each case, the main difficulty lies in the fact that the variables of interest are not directly observed.The first part deals with a mixed linear model for which repeated observations are available.The second part focuses on stochastic differential equations with random effects. Many trajectories are observed continuously on the same time interval.The third part is in a full multiplicative noise framework.The parts of the thesis are connected by the same context of inverse problems and by a common problematic: the estimation of the density function of a hidden variable.In the first two parts the density of one or two random effects is estimated. In the third part the goal is to rebuild the density of the original variable from the noisy observations.Different global methods are used and lead to well competitive estimators: kernel estimators, projection estimators or estimators built from deconvolution.Parameter selection gives adaptive estimators and the integrated risks are bounded using a Talagrand concentration inequality.A simulation study for each proposed estimator highlights their performances.A neuronal dataset is investigated with the new procedures for stochastic differential equations developed in this work
Cuesta, Ramirez Jhouben Janyk. "Optimization of a computationally expensive simulator with quantitative and qualitative inputs". Thesis, Lyon, 2022. http://www.theses.fr/2022LYSEM010.
Pełny tekst źródłaIn this thesis, costly mixed problems are approached through gaussian processes where the discrete variables are relaxed into continuous latent variables. the continuous space is more easily harvested by classical bayesian optimization techniques than a mixed space would. discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented lagrangians. several possible implementations of such bayesian mixed optimizers are compared. in particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. among the algorithms involving latent variables and an augmented lagrangian, a particular attention is devoted to the lagrange multipliers for which a local and a global estimation techniques are studied. the comparisons are based on the repeated optimization of three analytical functions and a mechanical application regarding a beam design. an additional study for applying a proposed mixed optimization strategy in the field of mixed self-calibration is made. this analysis was inspired in an application in radionuclide quantification, which defined an specific inverse function that required the study of its multiple properties in the continuous scenario. a proposition of different deterministic and bayesian strategies was made towards a complete definition in a mixed variable setup
Giacofci, Joyce. "Classification non supervisée et sélection de variables dans les modèles mixtes fonctionnels. Applications à la biologie moléculaire". Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM025/document.
Pełny tekst źródłaMore and more scientific studies yield to the collection of a large amount of data that consist of sets of curves recorded on individuals. These data can be seen as an extension of longitudinal data in high dimension and are often modeled as functional data in a mixed-effects framework. In a first part we focus on performing unsupervised clustering of these curves in the presence of inter-individual variability. To this end, we develop a new procedure based on a wavelet representation of the model, for both fixed and random effects. Our approach follows two steps : a dimension reduction step, based on wavelet thresholding techniques, is first performed. Then a clustering step is applied on the selected coefficients. An EM-algorithm is used for maximum likelihood estimation of parameters. The properties of the overall procedure are validated by an extensive simulation study. We also illustrate our method on high throughput molecular data (omics data) like microarray CGH or mass spectrometry data. Our procedure is available through the R package "curvclust", available on the CRAN website. In a second part, we concentrate on estimation and dimension reduction issues in the mixed-effects functional framework. Two distinct approaches are developed according to these issues. The first approach deals with parameters estimation in a non parametrical setting. We demonstrate that the functional fixed effects estimator based on wavelet thresholding techniques achieves the expected rate of convergence toward the true function. The second approach is dedicated to the selection of both fixed and random effects. We propose a method based on a penalized likelihood criterion with SCAD penalties for the estimation and the selection of both fixed effects and random effects variances. In the context of variable selection we prove that the penalized estimators enjoy the oracle property when the signal size diverges with the sample size. A simulation study is carried out to assess the behaviour of the two proposed approaches
Vasudevan, S. "Development of new spatially curved non-linear frame finite element using a mixed variational principle and rotations as independent variables". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13069.
Pełny tekst źródłaHay, John Leslie. "Statistical modelling for non-Gaussian time series data with explanatory variables". Thesis, Queensland University of Technology, 1999.
Znajdź pełny tekst źródłaZhang, Yang [Verfasser], Horst [Akademischer Betreuer] Baier i Kai-Uwe [Akademischer Betreuer] Bletzinger. "Efficient Procedures for Structural Optimization with Integer and Mixed-Integer Design Variables / Yang Zhang. Gutachter: Horst Baier ; Kai-Uwe Bletzinger. Betreuer: Horst Baier". München : Universitätsbibliothek der TU München, 2015. http://d-nb.info/1071948083/34.
Pełny tekst źródłaHartung, Julie A. "“It’s never going to be perfect even though I want it to be”: Quantitatively and qualitatively investigating honors and non-honors students’ experiences of perfectionism and related variables". Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/honors/631.
Pełny tekst źródłaZhang, Dan. "Business-to-Business (B2B) media in UK : a mixed methods study using product variables to assess the impacts of social media on product strategies". Thesis, University of Westminster, 2016. https://westminsterresearch.westminster.ac.uk/item/q10qy/business-to-business-b2b-media-in-uk-a-mixed-methods-study-using-product-variables-to-assess-the-impacts-of-social-media-on-product-strategies.
Pełny tekst źródłaZulian, Marine. "Méthodes de sélection et de validation de modèles à effets mixtes pour la médecine génomique". Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX003.
Pełny tekst źródłaThe study of complex biological phenomena such as human pathophysiology, pharmacokinetics of a drug or its pharmacodynamics can be enriched by modelling and simulation approaches. Technological advances in genetics allow the establishment of data sets from larger and more heterogeneous populations. The challenge is then to develop tools that integrate genomic and phenotypic data to explain inter-individual variability. In this thesis, we develop methods that take into account the complexity of biological data and the complexity of underlying processes. Curation steps of genomic covariates allow us to limit the number of potential covariates and limit correlations between covariates. We propose an algorithm for selecting covariates in a mixed effects model whose structure is constrained by the physiological process. In particular, we illustrate the developed methods on two medical applications: actual high blood pressure data and simulated tramadol (opioid) metabolism data
Hannachi, Marwa. "Placement des tâches matérielles de tailles variables sur des architectures reconfigurables dynamiquement et partiellement". Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0297/document.
Pełny tekst źródłaAdaptive systems based on Field-Programmable Gate Arrays (FPGA) architectures can benefit greatly from the high degree of flexibility offered by dynamic partial reconfiguration (DPR). Thanks to DPR, hardware tasks composing an adaptive system can be allocated and relocated on demand or depending on the dynamically changing environment. Existing design flows and commercial tools have evolved to meet the requirements of reconfigurables architectures, but that are limited in functionality. These tools do not allow an efficient placement and relocation of variable-sized hardware tasks. The main objective of this thesis is to propose a new methodology and a new approaches to facilitate to the designers the design phase of an adaptive and reconfigurable system and to make it operational, valid, optimized and adapted to dynamic changes in the environment. The first contribution of this thesis deals with the issues of relocation of variable-sized hardware tasks. A design methodology is proposed to address a major problem of relocation mechanisms: storing a single configuration bitstream to reduce memory requirements and increasing the reusability of generating hardware modules. A reconfigurable region partitioning technique is applied in this proposed relocation methodology to increase the efficiency of use of hardware resources in the case of reconfigurable tasks of variable sizes. This methodology also takes into account communication between different reconfigurable regions and the static region. To validate the design method, several cases studies are implemented. This validation shows an efficient use of hardware resources and a significant reduction in reconfiguration time. The second part of this thesis presents and details a mathematical formulations in order to automate the floorplanning of the reconfigurable regions in the FPGAs. The algorithms presented in this thesis are based on the optimization technique MILP (mixed integer linear programming). These algorithms allow to define automatically the location, the size and the shape of the dynamic reconfigurable region. We are mainly interested in this research to satisfy the constraints of placement of the reconfigurable zones and those related to the relocation. In addition, we consider the optimization of the hardware resources in the FPGA taking into account the tasks of variable sizes. Finally, an evaluation of the proposed approach is presented
Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.
Pełny tekst źródłaThis thesis consists of three chapters on longitudinal data analysis. Linear mixed models are discussed, both random effects (where individual intercepts are interpreted as random variables) and fixed effects (where individual intercepts are considered unknown constants, i.e., they must be estimated). Furthermore, robust models (resistant to outliers) and efficient models (with low estimator variability) are proposed in the scope of repeated measures. The second part of the thesis is dedicated to quantile regression, which explores the full conditional distribution of an outcome given its predictors. It introduces a more general method for dealing with heteroscedastic variables and longitudinal data. The first chapter is motivated by evaluating the statistical association between air pollution exposure and children and adolescents' lung ability among six months. A robust linear mixed model combined with an equally robust principal component analysis is proposed to deal with multicollinearity between covariates and the impact of extreme observations on the estimates. Huber and Tukey loss functions (M-estimation examples) are considered to obtain more robust estimators than the least squared function usually used to estimate the parameters of linear mixed models. A finite sample size study is carried out in the case where the covariates follow linear time series models with or without additive outliers. The impact of time correlation and outliers on fixed effect parameter estimates in linear mixed models is investigated. In addition, weights are introduced to reduce the estimates' bias even more. The study of the real data revealed that the robust principal component analysis exhibits three principal components explaining more than 90% of the total variability. The second principal component, which corresponds to particles smaller than 10 microns, significantly affects respiratory capacity. In addition, biological indicators such as passive smoking have a negative and significant effect on children's lung ability. The second chapter analyses fixed effect panel data with three different loss functions. To avoid the number of parameters increases with the sample size, we propose to penalize each regression method with the least absolute shrinkage and selection operator (LASSO). The asymptotic properties of two of these new techniques are established. A Monte Carlo study is performed for homoscedastic and heteroscedastic models. Although the model is more challenging to estimate in the heteroscedastic case for most statistical methods, the proposed methods perform well in both scenarios. This confirms that the proposed quantile regression methods are robust to heteroscedasticity. Their performance is tested on economic panel data from the Organisation for Economic Cooperation and Development (OECD). The objective of the third chapter is to simultaneously restrict the number of individual regression constants and explanatory covariates. In addition to the LASSO, an adaptive LASSO is proposed, which enjoys oracle proprieties, i.e., it owns the asymptotic selection of the true model if it exists, and it has the classical asymptotic normality property. Monte Carlo simulations are performed in the case of low dimensionality (much more observations than parameters) and in the case of moderate dimensionality (equivalent number of observations and parameters). In both cases, the adaptive method performs much better than the non-adaptive methods. Finally, we apply our methodology to a cohort dataset of moderate dimensionality. For each chapter, open-source software is written, which is available to the scientific community
Esta tese consiste em três capítulos sobre análise de dados longitudinais. São discutidos modelos lineares mistos, tanto efeitos aleatórios (onde interseptos individuais são interpretados como variáveis aleatórias) quanto efeitos fixos (onde interseptos individuais são considerados constantes desconhecidas, ou seja, devem ser estimadas). Além disso, modelos robustos (resistentes a outliers) e modelos eficientes (com baixa variabilidade de estimadores) são propostos no âmbito de medidas repetidas. A segunda parte da tese é dedicada à regressão quantílica, que explora toda a distribuição condicional de uma variável resposta dado suas preditoras. Ela introduz um método mais geral para lidar com variáveis heterocedásticas e dados longitudinais. O primeiro capítulo é motivado pela avaliação da associação estatística entre a exposição à poluição do ar e a capacidade pulmonar de crianças e adolescentes durante um período de seis meses. Um modelo linear misto robusto combinado com uma análise de componentes principais igualmente robusta é proposto para lidar com a multicolinearidade entre covariáveis e o impacto de observações extremas sobre as estimativas. As funções de perda Huber e Tukey (exemplos de \textit{M-estimation}) são consideradas para obter estimadores mais robustos do que a função de mínimos quadrados geralmente usada para estimar os parâmetros de modelos lineares mistos. Um estudo de tamanho de amostra finito é realizado no caso em que as covariáveis seguem modelos de séries temporais lineares com ou sem outliers aditivos. É investigado o impacto da correlação temporal e outliers nas estimativas de parâmetros de efeito fixo em modelos lineares mistos. Além disso, foram introduzidos pesos para reduzir ainda mais o enviesamento das estimativas. Um estudo em dados reais revelou que a análise robusta dos componentes principais apresenta três componentes principais que explicam mais de 90% da variabilidade total. O segundo componente principal, que corresponde a partículas menores que 10 micrômetros, afeta significativamente a capacidade respiratória. Além disso, os indicadores biológicos como o tabagismo passivo têm um efeito negativo e significativo na capacidade pulmonar das crianças. O segundo capítulo analisa dados de painel com efeito fixo com três diferentes funções de perda. Para evitar que o número de parâmetros aumente com o tamanho da amostra, propomos penalizar cada método de regressão com least absolute shrinkage and selection operator (LASSO). As propriedades assimptóticas de duas dessas novas técnicas são estabelecidas. Um estudo de Monte Carlo é realizado para modelos homocedásticos e heterosecásticos. Embora o modelo seja mais difícil de estimar no caso heterocedástico para a maioria dos métodos estatísticos, os métodos propostos têm bom desempenho em ambos os cenários. Isto confirma que os métodos de regressão quantílica propostos são robustos à heterocedasticidade. Seu desempenho é testado nos dados do painel econômico da Organização para Cooperação e Desenvolvimento Econômico (OCDE). O objetivo do terceiro capítulo é restringir simultaneamente o número de constantes de regressão individuais e covariáveis explicativas. Além do LASSO, é proposto um LASSO adaptativo que permite a seleção assimptótica do modelo verdadeiro, se este existir, e que desfruta da propriedade de normalidade assimptótica clássica. As simulações de Monte Carlo são realizadas no caso de baixa dimensionalidade (muito mais observações do que parâmetros) e no caso de dimensionalidade moderada (número equivalente de observações e parâmetros). Em ambos os casos, o método adaptativo tem um desempenho muito melhor do que os métodos não adaptativos. Finalmente, aplicamos nossa metodologia em um conjunto de dados de coorte de dimensionalidade moderada. Para cada capítulo, um software de código aberto é escrito e colocado à disposição da comunidade científica
Morice, Erwan. "Fissuration dans les matériaux quasi-fragiles : approche numérique et expérimentale pour la détermination d'un modèle incrémental à variables condensées". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2014. http://tel.archives-ouvertes.fr/tel-01048626.
Pełny tekst źródłaBaragatti, Meïli. "Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance". Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22100/document.
Pełny tekst źródłaThis thesis is divided into two main parts. In the first part, we propose a Bayesian variable selection method for probit mixed models. The objective is to select few relevant variables among tens of thousands while taking into account the design of a study, and in particular the fact that several datasets are merged together. The probit mixed model used is considered as part of a larger hierarchical Bayesian model, and the dataset is introduced as a random effect. The proposed method extends a work of Lee et al. (2003). The first step is to specify the model and prior distributions. In particular, we use the g-prior of Zellner (1986) for the fixed regression coefficients. In a second step, we use a Metropolis-within-Gibbs algorithm combined with the grouping (or blocking) technique of Liu (1994). This choice has both theoritical and practical advantages. The method developed is applied to merged microarray datasets of patients with breast cancer. However, this method has a limit: the covariance matrix involved in the g-prior should not be singular. But there are two standard cases in which it is singular: if the number of observations is lower than the number of variables, or if some variables are linear combinations of others. In such situations we propose to modify the g-prior by introducing a ridge parameter, and a simple way to choose the associated hyper-parameters. The prior obtained is a compromise between the conditional independent case of the coefficient regressors and the automatic scaling advantage offered by the g-prior, and can be linked to the work of Gupta and Ibrahim (2007).In the second part, we develop two new population-based MCMC methods. In cases of complex models with several parameters, but whose likelihood can be computed, the Equi-Energy Sampler (EES) of Kou et al. (2006) seems to be more efficient than the Parallel Tempering (PT) algorithm introduced by Geyer (1991). However it is difficult to use in combination with a Gibbs sampler, and it necessitates increased storage. We propose an algorithm combining the PT with the principle of exchange moves between chains with same levels of energy, in the spirit of the EES. This adaptation which we are calling Parallel Tempering with Equi-Energy Move (PTEEM) keeps the original idea of the EES method while ensuring good theoretical properties and a practical use in combination with a Gibbs sampler.Then, in some complex models whose likelihood is analytically or computationally intractable, the inference can be difficult. Several likelihood-free methods (or Approximate Bayesian Computational Methods) have been developed. We propose a new algorithm, the Likelihood Free-Parallel Tempering, based on the MCMC theory and on a population of chains, by using an analogy with the Parallel Tempering algorithm
Tchapnga, Takoudjou Rodrigue. "Méthodes de modélisation et d'optimisation par recherche à voisinages variables pour le problème de collecte et de livraison avec transbordement". Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0052/document.
Pełny tekst źródłaThe thesis is conducted under the ANR project PRODIGE and it is focused on seeking strategies allowing the optimization of transport in general and road freight transport in particular. The transportation problem support for this study is the pickup and delivery problem with transshipment.This problem generalizes several classical transportation problems.Transshipment is used as optimization and flexibility leverage. To study and solve this problem, analyzes are performed along three axes :the first objective concerns the development of an analytical model, more accurately a mathematical model with mixed variables. This model allows providing optimal solution to the decision maker, but has the disadvantage of requiring a time resolution that grows exponentially with the size of the problem. This limitation is overcome by the second line of the study that solves the transportation problem studied by an approximate optimization method while ensuring satisfactory solutions. The method used is a mataheuristic broadly followed the variables neighborhoods research principles. In the third objective, the overall results obtained in the thesis are tested in real transport situation via the PRODIGE project
Labenne, Amaury. "Méthodes de réduction de dimension pour la construction d'indicateurs de qualité de vie". Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0239/document.
Pełny tekst źródłaThe purpose of this thesis is to develop and suggest new dimensionreduction methods to construct composite indicators on a municipal scale. The developedstatistical methodology highlights the consideration of the multi-dimensionalityof the quality of life concept, with a particular attention on the treatment of mixeddata (quantitative and qualitative variables) and the introduction of environmentalconditions. We opt for a variable clustering approach and for a multi-table method(multiple factorial analysis for mixed data). These two methods allow to build compositeindicators that we propose as a measure of living conditions at the municipalscale. In order to facilitate the interpretation of the created composite indicators, weintroduce a method of selections of variables based on a bootstrap approach. Finally,we suggest the clustering of observations method, named hclustgeo, which integratesgeographical proximity constraints in the clustering procedure, in order to apprehendthe spatiality specificities better
Jackson, Tracey. "Applying the Multiple Constituents’ Model and Social Justice Variables to Determine the Constituents’ Perception of the Virginia Putative Father Registry". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2974.
Pełny tekst źródłaNizard, David. "Programmation mathématique non convexe non linéaire en variables entières : un exemple d'application au problème de l'écoulement de larges blocs d'actifs". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG015.
Pełny tekst źródłaMathematical programming provides a framework to study and resolve optimization problems, constrained or not. It represents an active domain of Applied Mathematics, for the second half of the 20th century.The aim of this thesis is to solve an non convex, non linear, pure integer, mathematical program, under a linear constraint of equality. This problem, although studied in this dissertation only in the deterministic case, stems from a financial application, known as the large block sale problem, or optimal portfolio liquidation. It consists in selling a (very large) known quantity M of a financial asset in finite time, discretized in N points in time, while maximizing the proceeds of the sale. At each point in time, the sell price is modeled by a penalty function, which reflects the antagonistic behavior of the market in response to our progressive selling flow.From the standpoint of the mathematical programming, this class of problems is NP-hard to solve according to Garey and Johnson, because the non convexity of the objective function imposes on us to adapt classical resolutions methods (Branch and Bound, cuts) for integer variables. In addition, as no general resolution method for this class of problems is known, the methods used for solving must be adapted to the problem specifics.The first part of the thesis is devoted to solve the problem, either exactly or approximately, using Dynamic Programming. We indeed prove that Bellman's equation applies to the problem studied and thus enables to solve it exactly and quickly for small instances. For medium and large instances, for which Dynamic Programming is either not available and/or efficient, we provide lower bounds using different heuristics relying on Dynamic Programming, or local search methods, for which performance (tightness and CPU time) and complexity are studied.The second part of this thesis focuses on the equivalent reformulation of the problem in a factored form, and on its convex relaxation using McCormick's inequalities. We introduce two exact resolution algorithms, which belongs to the Branch and Bound category. They return the global optimum or bound it in limited time.In a third part, dedicated to numerical experiments, we compare our resolution methods between each other and to state of the art solvers. We notice in particular that our bounds are comparable and sometimes even better than solvers' bounds, both free and commercial (e.g LocalSolver, Scip, Baron, Couenne et Bonmin), which we use as benchmark.In addition, we show that our resolution methods may apply to sufficiently regular and increasing penalty functions, especially functions which are currently not handled by some solvers, even though they make economic sense for the problem, as does trigonometric functions or the arctangent function for instance.Numerically, Dynamic Programming does optimally solve the problem, within a minute, for instances of size N<100 and M< 10 000. Our heuristics provide very tight lower bounds, which often reach the optimum, for N<1 000 and M<100 000. By contrast, optimal resolution of the factored problem proves efficient for instances of size N<10, M<1 000, even though we obtain relatively good upper bounds. Lastly, for large instances (M>1 000 000), our heuristics based on Dynamic Programming, when available, return the best lower bounds. However, we are not able to bound the optimum tightly, since our upper bounds are not thin
Lobato, Rafael Durbano. "Algoritmos para problemas de programação não-linear com variáveis inteiras e contínuas". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-06072009-130912/.
Pełny tekst źródłaMany optimization problems contain both integer and continuous variables and can be modeled as mixed-integer nonlinear programming problems. Problems of this nature appear frequently in chemical engineering and include, for instance, process synthesis, design of distillation columns, heat exchanger network synthesis and oil and gas production. In this work, we present algorithms based on Augmented Lagrangians and branch and bound for solving mixed-integer nonlinear programming problems. Two approaches are considered. In the first one, an Augmented Lagrangian algorithm is used for solving nonlinear programming problems that appear at each node in the branch and bound method. In the second approach, we use a branch and bound method for solving box-constrained problems with integer variables that appear as subproblems of the Augmented Lagrangian algorithm. Both algorithms guarantee to find an optimal solution for convex problems and have appropriate strategies to deal with non-convex problems, although there is no guarantee of optimality in this case. We present a problem of packing rectangles within an arbitrary convex region and propose models for this problem that result in nonlinear programs with integer and continuous variables. We have performed some numerical experiments and compared the results reached by the method described in this work and the results obtained by other methods. We have also performed experiments with mixed-integer nonlinear programming problems found in the literature and compared the performance of our method to that of other method publicly available.
Anderson, Mary E. "Financial Aid and Other Selected Variables Related to the Retention of First-Time Full-Time College Freshmen and their Persistence to Graduation Within Six Years at a Private Historically Black College or University". DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2016. http://digitalcommons.auctr.edu/cauetds/43.
Pełny tekst źródłaPham, Viet Nga. "Programmation DC et DCA pour l'optimisation non convexe/optimisation globale en variables mixtes entières : Codes et Applications". Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00833570.
Pełny tekst źródłaRen, Xuchun. "Novel computational methods for stochastic design optimization of high-dimensional complex systems". Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1738.
Pełny tekst źródłaMitjana, Florian. "Optimisation topologique de structures sous contraintes de flambage". Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30343/document.
Pełny tekst źródłaTopology optimization aims to design a structure by seeking the optimal material layout within a given design space, thus making it possible to propose innovative optimal designs. This thesis focuses on topology optimization for structural problems taking into account buckling constraints. In a wide variety of engineering fields, innovative structural design is crucial. The lightening of structures during the design phase holds a prominent place in order to reduce manufacturing costs. Thus the goal is often the minimization of the mass of the structure to be designed. Regarding the constraints, in addition to the conventional mechanical constraints (compression, tension), it is necessary to take into account buckling phenomena which are characterized by an amplification of the deformations of the structure and a potential annihilation of the capabilities of the structure to support the applied efforts. In order to adress a wide range of topology optimization problems, we consider the two types of representation of a structure: lattice structures and continuous structures. In the framework of lattice structures, the objective is to minimize the mass by optimizing the number of elements of the structure and the dimensions of the cross sections associated to these elements. We consider structures constituted by a set of frame elements and we introduce a formulation of the problem as a mixed-integer nonlinear problem. In order to obtain a manufacturable structure, we propose a cost function combining the mass and the sum of the second moments of inertia of each frame. We developed an algorithm adapted to the considered optimization problem. The numerical results show that the proposed approach leads to significant mass gains over existing approaches. In the case of continuous structures, topology optimization aims to discretize the design domain and to determine the elements of this discretized domain that must be composed of material, thus defining a discrete optimization problem. [...]
Tran, Vuong. "Bayesian variable selection in linear mixed effects models". Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139069.
Pełny tekst źródłaSocha, Krzysztof. "Ant colony optimization for continuous and mixed-variable domains". Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210533.
Pełny tekst źródłaFollowing this, we present the results of numerous simulations and testing. We compare the results obtained by the proposed algorithm on typical benchmark problems with those obtained by other methods used for tackling continuous optimization problems in the literature. Finally, we investigate how our algorithm performs on a real-world problem coming from the medical field—we use our algorithm for training neural network used for pattern classification in disease recognition.
Following an extensive analysis of the performance of ACO extended to continuous domains, we present how it may be further adapted to handle both continuous and discrete variables simultaneously. We thus introduce the first native mixed-variable version of an ACO algorithm. Then, we analyze and compare the performance of both continuous and mixed-variable
ACO algorithms on different benchmark problems from the literature. Through the research performed, we gain some insight into the relationship between the formulation of mixed-variable problems, and the best methods to tackle them. Furthermore, we demonstrate that the performance of ACO on various real-world mixed-variable optimization problems coming from the mechanical engineering field is comparable to the state of the art.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
Lan, Lan. "Variable Selection in Linear Mixed Model for Longitudinal Data". NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-05172006-211924/.
Pełny tekst źródłaIshizaki, Masato. "Mixed-initiative natural language dialogue with variable communicative modes". Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/518.
Pełny tekst źródłaLuinstra, Wayne Foster. "The effect of process variables on mixer-settler performance". Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5923.
Pełny tekst źródłaOmer, Jérémy Jean Guy. "Modèles déterministes et stochastiques pour la résolution numérique du problème de maintien de séparation entre aéronefs". Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0007/document.
Pełny tekst źródłaThis thesis belongs to the field of mathematical programming, applied to the separation of aircraft stabilised on the same altitude. The primary objective is to develop algorithms for the resolution of air conflicts. The expected benefit of such algorithm is to increase the capacity of the airspace in order to reduce the number of late flights and let more aircraft follow their optimal trajectory. Moreover, meteorological forecast and trajectory predictions being inexact,the uncertainty on the data is an important issue. The approach that is followed focuses on the deterministic problem in the first place because it is much simpler. To do this, four nonlinear and mixed integer linear programming models, including a criterion based on fuel consumption and flight duration, are developed. Their comparison on a benchmark of scenarios shows the relevance of using an approximate linear model for the study of the problem with uncertainties.A random wind field, correlated in space and time, as well as speed measures with Gaussianerrors are then taken into account. As a first step, the deterministic problem is adapted by computinga margin from an approximate calculation of conflict probabilities and by adding it tothe reference separation distance. Finally, a stochastic formulation with recourse is developed.In this model, the random errors are explicitly included in order to consider the possibility of ordering recourse actions if the observed errors cause new conflicts
Alabiso, Audry. "Linear Mixed Model Selection by Partial Correlation". Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587142724497829.
Pełny tekst źródłaVanderlinde, Jeferson Back [UNESP]. "Planejamento da expansão de sistemas de transmissão usando técnicas especializadas de programação inteira mista". Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152089.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver.
In this research, the theoretical analysis and computational implementation of the specialized dual simplex algorithm (DSA) and primal simplex algorithm (PSA) for bounded variables is considered. These algorithms have been incorporated in a Branch and Bound (B&B) algorithm to solve the Transmission Network Expansion Planning (TNEP) problem. In this case, the TNEP problem is modeled using transportation model and linear disjunctive model (DM), which produces a mixed-integer linear programming (MILP) problem. After relaxing the integrality of investment variables of the original MILP problem, the PSA is used to solve the initial linear programming (LP) problem. Also, it has been implemented a strategy in PSA to reduce the number of artificial variables which are added into the LP problem, and consequently reduces the number of iterations of PSA. Through optimal solution of the initial LP, the DSA is used in efficient reoptimization of subproblems, resulting from the B&B algorithm, thus excludes the need for complete resolution of each subproblems, which results reducing the CPU time and memory consumption. This research presents the implementation of the proposed approach using the FORTRAN programming language which operates independently and does not use any commercial solver.
Vanderlinde, Jeferson Back. "Planejamento da expansão de sistemas de transmissão usando técnicas especializadas de programação inteira mista /". Ilha Solteira, 2017. http://hdl.handle.net/11449/152089.
Pełny tekst źródłaResumo: Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver.
Doutor