In this work minimally supported D-optimal designs for response surface models with spatially correlated errors are studied. The spatially correlated errors describe the correlation between two measurements depending on their distance d through the covariance function C(d)=exp(-rd). In one dimensional design space, the minimally supported D-optimal designs for polynomial models with spatially correlated errors include two end points and are symmetric to the center of the design region. Exact solutions for simple linear and quadratic regression models are presented. For models with third or higher order, numerical solutions are given. While in two dimensional design space, the minimally supported D-optimal designs are invariant under translation¡Brotation and reflection. Numerical results show that a regular triangle on the experimental region of a circle is a minimally supported D-optimal design for the first-order response surface model.

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Orientadores: Nancy Lopes Garcia, Ronaldo Dias / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-18T04:43:13Z (GMT). No. of bitstreams: 1 Ludwig_GuilhermeVieiraNunes_M.pdf: 4540322 bytes, checksum: c767b4a6c7cd883a70e9ebbc33fe04ec (MD5) Previous issue date: 2011 / Resumo: O objetivo desta dissertação é desenvolver estimadores não paramétricos para a função de covariância de dados funcionais agregados, que consistem em combinações lineares de dados funcionais que não podem ser observados separadamente. Estes métodos devem ser capazes de produzir estimativas que separem a covariância típica de cada uma das subpopulações que geram os dados, e que sejam funções não negativas definidas. Sob estas restrições, foi definida uma classe de funções de covariância não estacionarias, à qual resultados da teoria de estimação de covariância de processos estacionários podem ser estendidos. Os métodos desenvolvidos foram ilustrados com a aplicação em dois problemas reais: a estimação do perfil de consumidores de energia elétrica, em função do tempo, e a estimação da transmitância de substâncias puras em espectroscopia de infravermelho, através da inspeção de misturas, em função do espectro da luz / Abstract: The goal of this dissertation is to develop nonparametric estimators for the covariance function of aggregated functional data, which consists into linear combinations of functional data that cannot be sampled separately. Such methods must be able to produce estimates that not only separate the typical covariance of the subpopulations composing the data, but also be nonnegative definite functions. Under these restrictions, a class of nonstationary covariance functions was proposed, to which stationary processes' covariance function estimation results can be readily extended. The developed methods were illustrated with an application to two real problems: the estimation of electric energy consumers' profiles, as a function of the time of the day, and the estimation of the transmittance of pure substances in infrared spectroscopy, while inspecting mixtures of them, as a function of light spectrum / Mestrado / Estatistica Não Parametrica / Mestre em Estatística

Orientador: Ronaldo Dias / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-13T14:28:48Z (GMT). No. of bitstreams: 1 Gomes_JoseCleltoBarros_M.pdf: 1798618 bytes, checksum: db671b29b83f0321e8dbc03c5af42cde (MD5) Previous issue date: 2009 / Resumo: O desafio na modelagem de processos espaciais está na descrição da estrutura de covariância do fenômeno sob estudo. Um estimador não-paramétrico da função de covariância foi construído de forma a usar combinações lineares de funções B-splines. Estas bases são usadas com muita frequência na literatura graças ao seu suporte compacto e a computação tão rápida quanto a habilidade de criar aproximações suaves e apropriadas. Verificouse que a função de covariância estimada era definida positiva por meio do teorema de Bochner. Para a estimação da função de covariância foi implementado um algoritmo que fornece um procedimento completamente automático baseado no número de funções bases. Então foram realizados estudos numéricos que evidenciaram que assintoticamente o procedimento é consistente, enquanto que para pequenas amostras deve-se considerar as restrições das funções de covariância. As funções de covariâncias usadas na estimação foram as de exponencial potência, gaussiana, cúbica, esférica, quadrática racional, ondular e família de Matérn. Foram estimadas ainda covariâncias encaixadas. Simulações foram realizadas também a fim de verificar o comportamento da distribuição da afinidade. As estimativas apresentaram-se satisfatórias / Abstract: The challenge in modeling of spatials processes is in description of the framework of covariance of the phenomenon about study. The estimation of covariance functions was done using a nonparametric linear combinations of basis functions B-splines. These bases are used frequently in literature thanks to its compact support and fast computing as the ability to create smooth and appropriate approaches There was positive definiteness of the estimator proposed by the Bochner's theorem. For the estimation of the covariance functions was implemented an algorithm that provides a fully automated procedure based on the number of basis functions. Then numerical studies were performed that showed that the procedure is consistent assynthotically. While for small samples should consider the restrictions of the covariance functions, so the process of optimization was non-linear optimization with restrictions. The following covariance functions were used in estimating: powered exponential, Gaussian, cubic, spherical, rational quadratic and Matérn family. Nested covariance funtions still were estimated. Simulations were also performed to verify the behavior of affinity and affinity partial, which measures how good is the true function of the estimated function. Estimates showed satisfactory / Mestrado / Mestre em Estatística

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Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences de données climatiques de longueur souhaitée avec des propriétés statistiques similaires aux données observées. Ces modèles sont de plus en plus utilisés en sciences du climat, hydrologie, agronomie. Cependant, peu de générateurs permettent de simuler plusieurs variables, dont les précipitations, en différents sites d’une région. Dans cette thèse, nous proposons un modèle original de générateur stochastique basé sur un champ gaussien multivarié spatio-temporel. Un premier travail méthodologique a été nécessaire pour développer un modèle de covariance croisée entièrement non séparable adapté à la nature spatio-temporelle multivariée des données étudiées. Cette covariance croisée est une généralisation au cas multivarié du modèle non séparable spatio-temporel de Gneiting dans le cas de la famille de Matérn. La démonstration de la validité du modèle et l’estimation de ses paramètres par maximum de vraisemblance par paires pondérées sont présentées. Une application sur des données climatiques démontre l’intérêt de ce nouveau modèle vis-à-vis des modèles existants. Le champ gaussien multivarié permet la modélisation des résidus des variables climatiques (hors précipitation). Les résidus sont obtenus après normalisation des variables par des moyennes et écarts-types saisonniers, eux-mêmes modélisés par des fonctions sinusoïdales. L’intégration des précipitations dans le générateur stochastique nécessite la transformation d’une composante du champ gaussien par une fonction d’anamorphose. Cette fonction d’anamorphose permet de gérer à la fois l’occurrence et l’intensité des précipitations. La composante correspondante du champ gaussien correspond ainsi à un potentiel de pluie, corrélé aux autres variables par la fonction de covariance croisée développée dans cette thèse. Notre générateur stochastique de temps a été testé sur un ensemble de 18 stations réparties en zone à climat méditerranéen (ou proche) en France. La simulation conditionnelle et non conditionnelle de variables climatiques journalières (températures minimales et maximales, vitesse moyenne du vent, rayonnement solaire et précipitation) pour ces 18 stations soulignent les bons résultats de notre modèle pour un certain nombre de statistiques / Stochastic weather generators are numerical models able to simulate sequences of weather data with similar statistical properties than observed data. However, few of them are able to simulate several variables (with precipitation) at different sites from one region. In this thesis, we propose an original model of stochastic generator based on a spatio-temporal multivariate Gaussian random field. A first methodological work was needed to develop a completely non separable cross-covariance function suitable for the spatio-temporal multivariate nature of studied data. This cross-covariance function is a generalization to the multivariate case of spatio-temporal non-separable Gneiting covariance in the case of the family of Matérn. The proof of the validity of the model and the estimation of its parameters by weighted pairwise maximum likelihood are presented. An application on weather data shows the interest of this new model compared with existing models. The multivariate Gaussian random field allows the modeling of weather variables residuals (excluding precipitation). Residuals are obtained after normalization of variables by seasonal means and standard deviations, themselves modeled by sinusoidal functions. The integration of precipitation in the stochastic generator requires the transformation of a component of the Gaussian random field by an anamorphosis function. This anamorphosis function can manage both the occurrence and intensity of precipitation. The corresponding component of the Gaussian random field corresponds to a rain potential, correlated with other variables by the cross-covariance function developed in this thesis. Our stochastic weather generator was tested on a set of 18 stations distributed over the Mediterranean area (or close) in France. The conditional and non-conditional simulation of daily weather variables (maximum and minimum temperature, average wind speed, solar radiation and precipitation) for these 18 stations show good result for a number of statistics.

Dans cette thèse, nous nous intéressons à l'estimation de la synchrone de consommation électrique (courbe moyenne). Etant donné que les variables étudiées sont fonctionnelles et que les capacités de stockage sont limitées et les coûts de transmission élevés, nous nous sommes intéressés à des méthodes d'estimation par sondage, alternatives intéressantes aux techniques de compression du signal. Nous étendons au cadre fonctionnel des méthodes d'estimation qui prennent en compte l'information auxiliaire disponible afin d'améliorer la précision de l'estimateur de Horvitz-Thompson de la courbe moyenne de consommation électrique. La première méthode fait intervenir l'information auxiliaire au niveau de l'estimation, la courbe moyenne est estimée à l'aide d'un estimateur basé sur un modèle de régression fonctionnelle. La deuxième l'utilise au niveau du plan de sondage, nous utilisons un plan à probabilités inégales à forte entropie puis l'estimateur de Horvitz-Thompson fonctionnel. Une estimation de la fonction de covariance est donnée par l'extension au cadre fonctionnel de l'approximation de la covariance donnée par Hájek. Nous justifions de manière rigoureuse leur utilisation par une étude asymptotique. Pour chacune de ces méthodes, nous donnons, sous de faibles hypothèses sur les probabilités d'inclusion et sur la régularité des trajectoires, les propriétés de convergence de l'estimateur de la courbe moyenne ainsi que de sa fonction de covariance. Nous établissons également un théorème central limite fonctionnel. Afin de contrôler la qualité de nos estimateurs, nous comparons deux méthodes de construction de bande de confiance sur un jeu de données de courbes de charge réelles. La première repose sur la simulation de processus gaussiens. Une justification asymptotique de cette méthode sera donnée pour chacun des estimateurs proposés. La deuxième utilise des techniques de bootstrap qui ont été adaptées afin de tenir compte du caractère fonctionnel des données / In this thesis, we are interested in estimating the mean electricity consumption curve. Since the study variable is functional and storage capacities are limited or transmission cost are high survey sampling techniques are interesting alternatives to signal compression techniques. We extend, in this functional framework, estimation methods that take into account available auxiliary information and that can improve the accuracy of the Horvitz-Thompson estimator of the mean trajectory. The first approach uses the auxiliary information at the estimation stage, the mean curve is estimated using model-assisted estimators with functional linear regression models. The second method involves the auxiliary information at the sampling stage, considering πps (unequal probability) sampling designs and the functional Horvitz-Thompson estimator. Under conditions on the entropy of the sampling design the covariance function of the Horvitz-Thompson estimator can be estimated with the Hájek approximation extended to the functional framework. For each method, we show, under weak hypotheses on the sampling design and the regularity of the trajectories, some asymptotic properties of the estimator of the mean curve and of its covariance function. We also establish a functional central limit theorem.Next, we compare two methods that can be used to build confidence bands. The first one is based on simulations of Gaussian processes and is assessed rigorously. The second one uses bootstrap techniques in a finite population framework which have been adapted to take into account the functional nature of the data

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