Tato práce se zabývá modely s promìnlivými koe cienty se za- mìøením na statistickou inferenci. Hlavní my¹lenkou tìchto modelù je pou¾ití regresních koe cientù, mìnících se v závislosti na nìjakém modi kátoru vlivu, namísto konstantních koe cientù klasické lineární regrese. Nejprve si de nujeme tyto modely a jejich odhadové procedury, kterých bylo doposud publikováno nì- kolik variant. K odhadu se pou¾ívá lokální regrese nebo rùzné druhy splajnù { vyhlazovací, polynomiální èi penalizované. Od metody odhadu se následnì od- víjí i daná statistická inference, ke které uvedeme odvozené vychýlení, rozptyl, asymptotickou normalitu, kon denèní pásma a testování hypotéz. Hlavním cílem na¹í práce je kompaktnì shrnout vybrané metody a jejich inferenci. Na závìr je navr¾ena proceduru pro výbìr promìnných.

Tato práce se zabývá modely s promìnlivými koe cienty se za- mìøením na statistickou inferenci. Hlavní my¹lenkou tìchto modelù je pou¾ití regresních koe cientù, mìnících se v závislosti na nìjakém modi kátoru vlivu, namísto konstantních koe cientù klasické lineární regrese. Nejprve si de nujeme tyto modely a jejich odhadové procedury, kterých bylo doposud publikováno nì- kolik variant. K odhadu se pou¾ívá lokální regrese nebo rùzné druhy splajnù { vyhlazovací, polynomiální èi penalizované. Od metody odhadu se následnì od- víjí i daná statistická inference, ke které uvedeme odvozené vychýlení, rozptyl, asymptotickou normalitu, kon denèní pásma a testování hypotéz. Hlavním cílem na¹í práce je kompaktnì shrnout vybrané metody a jejich inferenci. Na závìr je navr¾ena proceduru pro výbìr promìnných.

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Doctor of Philosophy / Department of Statistics / Weixin Yao / This dissertation contains two projects that are related to varying coefficient models. The traditional least squares based kernel estimates of the varying coefficient model will lose some efficiency when the error distribution is not normal. In the first project, we propose a novel adaptive estimation method that can adapt to different error distributions and provide an efficient EM algorithm to implement the proposed estimation. The asymptotic properties of the resulting estimator is established. Both simulation studies and real data examples are used to illustrate the finite sample performance of the new estimation procedure. The numerical results show that the gain of the adaptive procedure over the least squares estimation can be quite substantial for non-Gaussian errors. In the second project, we propose a unified inference for sparse and dense longitudinal data in time-varying coefficient models. The time-varying coefficient model is a special case of the varying coefficient model and is very useful in longitudinal/panel data analysis. A mixed-effects time-varying coefficient model is considered to account for the within subject correlation for longitudinal data. We show that when the kernel smoothing method is used to estimate the smooth functions in the time-varying coefficient model for sparse or dense longitudinal data, the asymptotic results of these two situations are essentially different. Therefore, a subjective choice between the sparse and dense cases may lead to wrong conclusions for statistical inference. In order to solve this problem, we establish a unified self-normalized central limit theorem, based on which a unified inference is proposed without deciding whether the data are sparse or dense. The effectiveness of the proposed unified inference is demonstrated through a simulation study and a real data application.

The aim of this thesis is to provide an overview of the varying coefficient mod- els - a class of regression models that allow the coefficients to vary as functions of random variables. This concept is described for independent samples, longi- tudinal data, and time series. Estimation methods include polynomial spline, smoothing spline, and local polynomial methods for models of a linear form and local maximum likelihood method for models of a generalized linear form. The statistical properties focus on the consistency and asymptotical distribution of the estimators. The numerical study compares the finite sample performance of the estimators of coefficient functions. 1

Les modèles physico-chimiques destinés à représenter la réalité expérimentale peuvent se révéler inadéquats. C'est le cas du piège à oxyde d'azote, utilisé comme support applicatif de notre thèse, qui est un système catalytique traitant les émissions polluantes du moteur Diesel. Les sorties sont des courbes de concentrations des polluants, qui sont des données fonctionnelles, dépendant de concentrations initiales scalaires.L'objectif initial de cette thèse est de proposer des plans d'expériences ayant un sens pour l'utilisateur. Cependant les plans d'expérience s'appuyant sur des modèles, l'essentiel du travail a conduit à proposer une représentation statistique tenant compte des connaissances des experts, et qui permette de construire ce plan.Trois axes de recherches ont été explorés. Nous avons d'abord considéré une modélisation non fonctionnelle avec le recours à la théorie du krigeage. Puis, nous avons pris en compte la dimension fonctionnelle des réponses, avec l'application et l'extension des modèles à coefficients variables. Enfin en repartant du modèle initial, nous avons fait dépendre les paramètres cinétiques des entrées (scalaires) à l'aide d'une représentation non paramétrique.Afin de comparer les méthodes, il a été nécessaire de mener une campagne expérimentale, et nous proposons une démarche de plan exploratoire, basée sur l’entropie maximale. / Physico-chemical models designed to represent experimental reality may prove to be inadequate. This is the case of nitrogen oxide trap, used as an application support of our thesis, which is a catalyst system treating the emissions of the diesel engine. The outputs are the curves of concentrations of pollutants, which are functional data, depending on scalar initial concentrations.The initial objective of this thesis is to propose experiental design that are meaningful to the user. However, the experimental design relying on models, most of the work has led us to propose a statistical representation taking into account the expert knowledge, and allows to build this plan.Three lines of research were explored. We first considered a non-functional modeling with the use of kriging theory. Then, we took into account the functional dimension of the responses, with the application and extension of varying coefficent models. Finally, starting again from the original model, we developped a model depending on the kinetic parameters of the inputs (scalar) using a nonparametric representation.To compare the methods, it was necessary to conduct an experimental campaign, and we propose an exploratory design approach, based on maximum entropy.