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Józef Marcinkiewicz: analysis and probability

N. H. Bingham (2011)

Banach Center Publications

We briefly review Marcinkiewicz's work, on analysis, on probability, and on the interplay between the two. Our emphasis is on the continuing vitality of Marcinkiewicz's work, as evidenced by its influence on the standard works. What is striking is how many of the themes that Marcinkiewicz studied (alone, or with Zygmund) are very much alive today. What this demonstrates is that Marcinkiewicz and Zygmund, as well as having extraordinary mathematical ability, also had excellent mathematical taste.

Limit laws for products of free and independent random variables

Hari Bercovici, Vittorino Pata (2000)

Studia Mathematica

We determine the distributional behavior of products of free (in the sense of Voiculescu) identically distributed random variables. Analogies and differences with the classical theory of independent random variables are then discussed.

Limit laws for transient random walks in random environment on

Nathanaël Enriquez, Christophe Sabot, Olivier Zindy (2009)

Annales de l’institut Fourier

We consider transient random walks in random environment on with zero asymptotic speed. A classical result of Kesten, Kozlov and Spitzer says that the hitting time of the level n converges in law, after a proper normalization, towards a positive stable law, but they do not obtain a description of its parameter. A different proof of this result is presented, that leads to a complete characterization of this stable law. The case of Dirichlet environment turns out to be remarkably explicit.

Limit theorems for some functionals with heavy tails of a discrete time Markov chain

Patrick Cattiaux, Mawaki Manou-Abi (2014)

ESAIM: Probability and Statistics

Consider an irreducible, aperiodic and positive recurrent discrete time Markov chain (Xn,n ≥ 0) with invariant distribution μ. We shall investigate the long time behaviour of some functionals of the chain, in particular the additive functional S n = i = 1 n f ( X i ) S n = ∑ i = 1 n f ( X i ) for a possibly non square integrable functionf. To this end we shall link ergodic properties of the chain to mixing properties, extending known results in the continuous time case. We will then use existing results of convergence...

Limit theorems in free probability theory II

Gennadii Chistyakov, Friedrich Götze (2008)

Open Mathematics

Based on an analytical approach to the definition of multiplicative free convolution on probability measures on the nonnegative line ℝ+ and on the unit circle 𝕋 we prove analogs of limit theorems for nonidentically distributed random variables in classical Probability Theory.

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