The aim of this thesis is a detailed analysis of selected approaches of unit root testing. First chapter deals with the basic knowledge of the theory of stochastic processes. Further, we describe Dickey-Fuller tests, t-tests and likelihood ratio tests for the presence of a unit root and derive their asymptotic properties. Numerical studies include comparison of accuracy of the parameter estimates, estimating quantiles of the presented distributions, their graphical presentation and determination of power of our tests. The acquired theoretical knowledge is applied on real data which were analyzed using software Mathematica and R. Powered by TCPDF (www.tcpdf.org)

IntroStat was designed to meet the needs of students, primarily those in business, commerce and management, for a course in applied statistics. IntroSTAT is designed as a lecture-book. One of the aims is to maximize the time spent in explaining concepts and doing examples. The book is commonly used as part of first year courses into Statistics.

<p>This dissertation establishes limit theory for topological functionals of geometric complexes from a stochastic process viewpoint. Standard filtrations of geometric complexes, such as the Čech and Vietoris-Rips complexes, have a natural parameter <i>r </i>which governs the formation of simplices: this is the basis for persistent homology. However, the parameter <i>r</i> may also be considered the time parameter of an appropriate stochastic process which summarizes the evolution of the filtration.</p><p>Here we examine the stochastic behavior of two of the foremost classes of topological functionals of such filtrations: the Betti numbers and the Euler characteristic. There are also two distinct setups in which the points underlying the complexes are generated, where the points are distributed randomly in <i>R^d</i> according to a general density (the traditional setup) and where the points lie in the tail of a heavy-tailed or exponentially-decaying “noise” distribution (the extreme-value theory (EVT) setup).<br></p><p>These results constitute some of the first results combining topological data analysis (TDA) and stochastic process theory. The first collection of results establishes stochastic process limits for Betti numbers of Čech complexes of Poisson and binomial point processes for two specific regimes in the traditional setup: the sparse regime—when the parameter <i>r </i>governing the formation of simplices causes the Betti numbers to concentrate on components of the lowest order; and the critical regime—when the parameter <i>r</i> is of the order <i>n^-1/d</i> and the geometric complex becomes highly connected with topological holes of every dimension. The second collection of results establishes a functional strong law of large numbers and a functional central limit theorem for the Euler characteristic of a random geometric complex for the critical regime in the traditional setup. The final collection of results establishes functional strong laws of large numbers for geometric complexes in the EVT setup for the two classes of “noise” densities mentioned above.<br></p>

The aim of this paper is to investigate the known nonuniform version of Esseen''s inequality in the Lp-metric, to get a numerical bound for the appearing constant L. For a long time the results given by several authors constate the impossibility of a nonuniform estimation in the most interesting case δ=1, because the effect L=L(δ)=O(1/(1-δ)), δ->1-0, was observed, where 2+δ, 0<δ<1, is the order of the assumed moments of the considered independent random variables X_k, k=1,2,...,n. Again making use of the method of conjugated distributions, we improve the well-known technique to show in the most interesting case δ=1 the finiteness of the absolute constant L and to prove L=L(1)=<127,74*7,31^(1/p), p>1. In the case 0<δ<1 we only give the analytical structure of L but omit numerical calculations. Finally an example on normal approximation of sums of l_2-valued random elements demonstrates the application of the nonuniform mean central limit bounds obtained here.:1. Introduction S. 3 2. The nonuniform version of ESSEEN''s Inequality in the Lp-metrie S. 4 3. The partition of the domain of integration S. 5 4. The domain of moderate x S. 8 5. An error bound for large values of L2+δ,n S. 12 6. The proof of the inequality (2.1) S. 13 7. An application to normalapproximation of sums of l2-valued random elements S. 14 References S. 18 / Das Anliegen dieses Artikels besteht in der Untersuchung einer bekannten Variante der Esseen''schen Ungleichung in Form einer ungleichmäßigen Fehlerabschätzung in der Lp-Metrik mit dem Ziel, eine numerische Abschätzung für die auftretende absolute Konstante L zu erhalten. Längere Zeit erweckten die Ergebnisse, die von verschiedenen Autoren angegeben wurden, den Eindruck, dass die ungleichmäßige Fehlerabschätzung im interessantesten Fall δ=1 nicht möglich wäre, weil auf Grund der geführten Beweisschritte der Einfluss von δ auf L in der Form L=L(δ)=O(1/(1-δ)), δ->1-0, beobachtet wurde, wobei 2+δ, 0<δ<1, die Ordnung der vorausgesetzten Momente der betrachteten unabhängigen Zufallsgrößen X_k, k=1,2,...,n, angibt. Erneut wird die Methode der konjugierten Verteilungen angewendet und die gut bekannte Beweistechnik verbessert, um im interessantesten Fall δ=1 die Endlichkeit der absoluten Konstanten L nachzuweisen und um zu zeigen, dass L=L(1)=<127,74*7,31^(1/p), p>1, gilt. Im Fall 0<δ<1 wird nur die analytische Struktur von L herausgearbeitet, jedoch ohne numerische Berechnungen. Schließlich wird mit einem Beispiel zur Normalapproximation von Summen l_2-wertigen Zufallselementen die Anwendung der gewichteten Fehlerabschätzung im globalen zentralen Grenzwertsatz demonstriert.:1. Introduction S. 3 2. The nonuniform version of ESSEEN''s Inequality in the Lp-metrie S. 4 3. The partition of the domain of integration S. 5 4. The domain of moderate x S. 8 5. An error bound for large values of L2+δ,n S. 12 6. The proof of the inequality (2.1) S. 13 7. An application to normalapproximation of sums of l2-valued random elements S. 14 References S. 18

No description available.

No description available.

[pt] Nessa dissertação vamos introduzir, elaborar e combinar ideias da Teoria de martingais, a Teoria de grafos aleatórios e o Teorema Central do Limite. Em particular, veremos como martingais podem ser usados para representar desvios de contagem de subgrafos. Usando esta representação e o Teorema Central do Limite para martingais, conseguiremos demonstrar um Teorema Central do Limite para a contagem de subgrafos livres de triângulos no grafo aleatório Erdos-Rényi G(n,m) . Além disso, nossa demonstração também nos trará informação sobre a taxa de convergência, mostrando que a distribuição dos desvios converge rapidamente para a distribuição normal. / [en] In this dissertation we shall introduce, elaborate and combine ideas from martingale Theory, random graph Theory and the Central Limit Theorem. In particular, we will see how martingales can be used to represent deviations of subgraph counts. Using this representation and the Central Limit Theorem for martingales, we will be able to demonstrate a Central Limit Theorem for the triangle-free subgraph count in the Erdos-Rényi G(n,m) random graph. Furthermore, our proof also gives us information about the rate of convergence, showing that the distribution of deviations converges rapidly to the normal distribution.

No description available.

L’objectif en arrière-plan est de montrer que plusieurs modèles de marches aléatoires en milieux aléatoires (MAMA) sont reliés à un modèle-jouet appelé le modèle de piège de Bouchaud. Le domaine des MAMA est très vaste, mais nous nous intéressons particulièrement à une classe de modèle où la marche est réversible et directionnellement transiente. En particulier, nous verrons pourquoi on pense que ces modèles se ressemblent et quel genre de similarités on s’attend à obtenir, une fois qu’on aura présenté le modèle de Bouchaud. Nous verrons aussi quelques techniques de base utilisés de ce domaine, telles que les temps de régénérations. Comme contribution, nous allons démontrer un théorème central limite pour la marche aléatoire β-biaisée sur un arbre de Galton-Watson. / This Master thesis is part of a larger project of linking the behaviours of a certain type of random walks in random environments (RWRE) with those of a toy model called the Bouchaud’s trap model. The domain of RWRE is very wide but our interest will be on a particular kind of models which are reversible and directionally transient. More specifically, we will see why those models have similar behaviours and what kind of results we could expect once we have reviewed the Bouchaud’s trap model. We will also present some basic technic used in this field, such as regeneration times. As a contribution, we will demonstrate a central limit theorem for the β-biased random walk on a Galton-Watson tree.

Le sujet principal de ce mémoire est l'étude de la distribution asymptotique de la fonction f_m qui compte le nombre de diviseurs premiers distincts parmi les nombres premiers $p_1,...,p_m$. Au premier chapitre, nous présentons les sept résultats qui seront démontrés au chapitre 4. Parmi ceux-ci figurent l'analogue du théorème d'Erdos-Kac et un résultat sur les grandes déviations. Au second chapitre, nous définissons les espaces de probabilités qui serviront à calculer les probabilités asymptotiques des événements considérés, et éventuellement à calculer les densités qui leur correspondent. Le troisième chapitre est la partie centrale du mémoire. On y définit la promenade aléatoire qui, une fois normalisée, convergera vers le mouvement brownien. De là, découleront les résultats qui formeront la base des démonstrations de ceux chapitre 1. / The main topic of this masters thesis is the study of the asymptotic distribution of the fonction f_m which counts the number of distinct prime divisors among the first $m$ prime numbers, i.e. $p_1,...,p_m$. The first chapter provides the seven main results which will later on be proved in chapter 4. Among these we find the analogue of the Erdos-Kac central limit theorem and a result on large deviations. In the following chapter, we define several probability spaces on which we will calculate asymptotic probabilities of specific events. These will become necessary for calculating their corresponding densities. The third chapter is the main part of this masters thesis. In it, we introduce a random walk which, when suitably normalized, will converge to the Brownian motion. We will then obtain results which will form the basis of the proofs of those of chapiter 1.