[PDF] Weak quenched limiting distributions for transient one

Infinite divisibility of solutions to some self-similar

668 P Pierre 1 Introduction During the last decade, there has been a renewed interest in self-similar semigroups, something which seems to be attributed to their connections to several fields of mathematics and more generally to many area of the sciences

J´er ˆome Bolte Aris Daniilidis Adrian Lewis Masahiro Shiota

measure zero Our w ork relies on concepts of generalized critical p oin ts in the sense nonsmo oth analysis that w e no pro ceed to describ e W sa y x∗ is a limiting sub gr adient for the con tin uous function f on Rn at x, and w e write x∗ ∈ ∂f(x) (the limiting sub di er ential of f), if there exist sequences xn → x and x∗ n

Soumik Pal - University of Washington

20 Concentration of measure for systems of Brownian particles interacting through their ranks With M SHKOLNIKOV The Annals of Applied Probability 24 (4), 1482–1508, 2014 21 Cycles and eigenvalues of sequentially growing random regular graphs With T JOHN-SON The Annals of Probability 42 (4), 1396–1437, 2014 22 On meteors, earthworms

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A B (Ton) Dieker Assistant Professor School for Industrial

-ORIE Seminar, Cornell University, April 1, 2008 -INFORMS Applied Probability Society Conference 2007, Eindhoven, The Netherlands, July 9, 2007 • On series Jackson networks and determinantal transition kernels -Math Probability Seminar, University of Wisconsin at Madison, November 15, 2007 -INFORMS Annual Meeting 2007,

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Annales de la Société Polonaise de Mathématique, Cracovie 6, 93-116 (in Johnson et al 1995) Geluk, J L and Peng, L (2000) An Adaptive Optimal Estimate of the Tail Index for MA(1) Time Series Statistics and Probability Letters 46, 217-227 Gnedenko, B V (1943) Sur la Distribution Limite du Terme d’une Serie Aleatoire

Weak quenched limiting distributions for transient one

probability measure P on Ω Given an environment ω ={ωx}x∈Z ∈Ω and an initial location x ∈Z,welet{Xn}n≥0 be the Markov chain with law Px ω defined by P ω x(X0 =x)=1, and Px ω(Xn+1 =zXn =y)= ωy,z=y +1, 1 −ωy,z=y −1, 0, otherwise Since the environment ω is random, Px ω(·) is a random probability measure and is called