Download Free Asymptotic Fluctuation Behavior Of Sums Of Weakly Dependent Random Variables Book in PDF and EPUB Free Download. You can read online Asymptotic Fluctuation Behavior Of Sums Of Weakly Dependent Random Variables and write the review.

A strong revival of interest in the law of the iterated logarithm and related asymptotic fluctuation results has occurred in the last decade, stimulated by two remarkable papers by Volker Strassen. In these papers, Strassen introduces a new method for establishing such fluctuation results for sums of independent random variables and for martingales. Strassen's almost sure invariance principle for martingales states that each martingale satisfying a certain second moment condition is with probability on "close" to a Brownian motion. In this monograph we investigate the asymptotic fluctuation behavior of sums of weakly dependent random variables, such as lacunary trigonometric mixing, and Gaussian sequences.
Limit theorems for random sequences may conventionally be divided into two large parts, one of them dealing with convergence of distributions (weak limit theorems) and the other, with almost sure convergence, that is to say, with asymptotic prop erties of almost all sample paths of the sequences involved (strong limit theorems). Although either of these directions is closely related to another one, each of them has its own range of specific problems, as well as the own methodology for solving the underlying problems. This book is devoted to the second of the above mentioned lines, which means that we study asymptotic behaviour of almost all sample paths of linearly transformed sums of independent random variables, vectors, and elements taking values in topological vector spaces. In the classical works of P.Levy, A.Ya.Khintchine, A.N.Kolmogorov, P.Hartman, A.Wintner, W.Feller, Yu.V.Prokhorov, and M.Loeve, the theory of almost sure asymptotic behaviour of increasing scalar-normed sums of independent random vari ables was constructed. This theory not only provides conditions of the almost sure convergence of series of independent random variables, but also studies different ver sions of the strong law of large numbers and the law of the iterated logarithm. One should point out that, even in this traditional framework, there are still problems which remain open, while many definitive results have been obtained quite recently.
For many practical problems, observations are not independent. In this book, limit behaviour of an important kind of dependent random variables, the so-called mixing random variables, is studied. Many profound results are given, which cover recent developments in this subject, such as basic properties of mixing variables, powerful probability and moment inequalities, weak convergence and strong convergence (approximation), limit behaviour of some statistics with a mixing sample, and many useful tools are provided. Audience: This volume will be of interest to researchers and graduate students in the field of probability and statistics, whose work involves dependent data (variables).
A comprehensive monograph presenting a unified systematic exposition of the large deviations theory for heavy-tailed random walks.