This thesis investigates the statistical properties of Kernel Smoothed Empirical Likelihood (KSEL, e.g. Smith, 1997 and 2004) estimator and various associated inference procedures in weakly dependent data. New tests for structural stability are proposed and analysed. Asymptotic analyses and Monte Carlo experiments are applied to assess these new tests, theoretically and empirically. Chapter 1 reviews and discusses some estimation and inferential properties of Empirical Likelihood (EL, Owen, 1988) for identically and independently distributed data and compares it with Generalised EL (GEL), GMM and other estimators. KSEL is extensively treated, by specialising kernel-smoothed GEL in the working paper of Smith (2004), some of whose results and proofs are extended and refined in Chapter 2. Asymptotic properties of some tests in Smith (2004) are also analysed under local alternatives. These special treatments on KSEL lay the foundation for analyses in Chapters 3 and 4, which would not otherwise follow straightforwardly. In Chapters 3 and 4, subsample KSEL estimators are proposed to assist the development of KSEL structural stability tests to diagnose for a given breakpoint and for an unknown breakpoint, respectively, based on relevant work using GMM (e.g. Hall and Sen, 1999; Andrews and Fair, 1988; Andrews and Ploberger, 1994). It is also original in these two chapters that moment functions are allowed to be kernel-smoothed after or before the sample split, and it is rigorously proved that these two smoothing orders are asymptotically equivalent. The overall null hypothesis of structural stability is decomposed according to the identifying and overidentifying restrictions, as Hall and Sen (1999) advocate in GMM, leading to a more practical and precise structural stability diagnosis procedure. In this framework, these KSEL structural stability tests are also proved via asymptotic analysis to be capable of identifying different sources of instability, arising from parameter value change or violation of overidentifying restrictions. The analyses show that these KSEL tests follow the same limit distributions as their counterparts using GMM. To examine the finite-sample performance of KSEL structural stability tests in comparison to GMM's, Monte Carlo simulations are conducted in Chapter 5 using a simple linear model considered by Hall and Sen (1999). This chapter details some relevant computational algorithms and permits different smoothing order, kernel type and prewhitening options. In general, simulation evidence seems to suggest that compared to GMM's tests, these newly proposed KSEL tests often perform comparably. However, in some cases, the sizes of these can be slightly larger, and the false null hypotheses are rejected with much higher frequencies. Thus, these KSEL based tests are valid theoretical and practical alternatives to GMM's.

Cette thèse est particulièrement destinée à traiter deux types de problèmes : clustering et l'alignement multiple de séquence. Notre objectif est de résoudre de manière satisfaisante ces problèmes globaux et de tester l'approche de la Programmation DC et DCA sur des jeux de données réelles. La thèse comporte trois parties : la première partie est consacrée aux nouvelles approches de l'optimisation non convexe. Nous y présentons une étude en profondeur de l'algorithme qui est utilisé dans cette thèse, à savoir la programmation DC et l'algorithme DC (DCA). Dans la deuxième partie, nous allons modéliser le problème clustering en trois sous-problèmes non convexes. Les deux premiers sous-problèmes se distinguent par rapport au choix de la norme utilisée, (clustering via les normes 1 et 2). Le troisième sous-problème utilise la méthode du noyau, (clustering via la méthode du noyau). La troisième partie sera consacrée à la bio-informatique. On va se focaliser sur la modélisation et la résolution de deux sous-problèmes : l'alignement multiple de séquence et l'alignement de séquence d'ARN par structure. Tous les chapitres excepté le premier se terminent par des tests numériques. / This Ph.D. thesis is particularly intended to treat two types of problems : clustering and the multiple alignment of sequence. Our objective is to solve efficiently these global problems and to test DC Programming approach and DCA on real datasets. The thesis is divided into three parts : the first part is devoted to the new approaches of nonconvex optimization-global optimization. We present it a study in depth of the algorithm which is used in this thesis, namely the programming DC and the algorithm DC ( DCA). In the second part, we will model the problem clustering in three nonconvex subproblems. The first two subproblems are distinguished compared to the choice from the norm used, (clustering via norm 1 and 2). The third subproblem uses the method of the kernel, (clustering via the method of the kernel). The third part will be devoted to bioinformatics, one goes this focused on the modeling and the resolution of two subproblems : the multiple alignment of sequence and the alignment of sequence of RNA. All the chapters except the first end in numerical tests.