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Matthias Meiners

Publications and source records attributed to Matthias Meiners.

29 records · Page 2Linked to original sources

Asymptotics of random processes with immigration I: scaling limits

Let $(X_1, ξ_1), (X_2,ξ_2),\ldots$ be i.i.d.~copies of a pair $(X,ξ)$ where $X$ is a random process with paths in the Skorokhod space $D[0,\infty)$ and $ξ$ is a positive random variable. Define $S_k := ξ_1+\ldots+ξ_k$, $k \in \mathbb{N}_0$ and $Y(t) := \sum_{k\geq 0} X_{k+1}(t-S_k) 1_{\{S_k \leq t\}}$, $t\geq 0$. We call the process $(Y(t))_{t \geq 0}$ random process with immigration at the epochs of a renewal process. We investigate weak convergence of the finite-dimensional distributions of $(Y(ut))_{u>0}$ as $t\to\infty$. Under the assumptions that the covariance function of $X$ is regularly varying in $(0,\infty)\times (0,\infty)$ in a uniform way, the class of limiting processes is rather rich and includes Gaussian processes with explicitly given covariance functions, fractionally integrated stable Lévy motions and their sums when the law of $ξ$ belongs to the domain of attraction of a stable law with finite mean, and conditionally Gaussian processes with explicitly given (conditional) covariance functions, fractionally integrated inverse stable subordinators and their sums when the law of $ξ$ belongs to the domain of attraction of a stable law with infinite mean.

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Solutions to complex smoothing equations

We consider smoothing equations of the form $$X ~\stackrel{\mathrm{law}}{=}~ \sum_{j \geq 1} T_j X_j + C$$ where $(C,T_1,T_2,\ldots)$ is a given sequence of random variables and $X_1,X_2,\ldots$ are independent copies of $X$ and independent of the sequence $(C,T_1,T_2,\ldots)$. The focus is on complex smoothing equations, i.e., the case where the random variables $X, C,T_1,T_2,\ldots$ are complex-valued, but also more general multivariate smoothing equations are considered, in which the $T_j$ are similarity matrices. Under mild assumptions on $(C,T_1,T_2,\ldots)$, we describe the laws of all random variables $X$ solving the above smoothing equation. These are the distributions of randomly shifted and stopped Lévy processes satisfying a certain invariance property called $(U,α)$-stability, which is related to operator (semi)stability. The results are applied to various examples from applied probability and statistical physics.

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Rate of convergence in the law of large numbers for supercritical general multi-type branching processes

We provide sufficient conditions for polynomial rate of convergence in the weak law of large numbers for supercritical general indecomposable multi-type branching processes. The main result is derived by investigating the embedded single-type process composed of all individuals having the same type as the ancestor. As an important intermediate step, we determine the (exact) polynomial rate of convergence of Nerman's martingale in continuous time to its limit. The techniques used also allow us to give streamlined proofs of the weak and strong laws of large numbers and ratio convergence for the processes in focus.

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Exponential moments of first passage times and related quantities for Lévy processes

For a Lévy process on the real line, we provide complete criteria for the finiteness of exponential moments of the first passage time into the interval $(r,\infty)$, the sojourn time in the interval $(-\infty,r]$, and the last exit time from $(-\infty,r]$. Moreover, whenever these quantities are finite, we derive their respective asymptotic behavior as $r \to \infty$.

math.PR

Fixed points of multivariate smoothing transforms with scalar weights

Given a sequence $(C_1,\ldots,C_d,T_1,T_2,\ldots)$ of real-valued random variables with $N := \#\{j \geq 1: T_j \not = 0\} < \infty$ almost surely, there is an associated smoothing transformation which maps a distribution $P$ on $\mathbb{R}^d$ to the distribution of $\sum_{j \geq 1} T_j \mathbf{X}^{(j)} + \mathbf{C}$ where $\mathbf{C} = (C_1,\ldots,C_d)$ and $(\mathbf{X}^{(j)})_{j \geq 1}$ is a sequence of independent random vectors with distribution $P$ independent of $(C_1,\ldots,C_d,T_1,T_2,\ldots)$. We are interested in the fixed points of this mapping. By improving on the techniques developed in [G. Alsmeyer, J.D. Biggins, and M. Meiners. The functional equation of the smoothing transform {\em Ann. Probab.}, 40(5):2069--2105, 2012] and [G. Alsmeyer and M. Meiners. Fixed points of the smoothing transform: two-sided solutions. {\em Probab. Theory Related Fields}, 155(1-2):165--199, 2013], we determine the set of all fixed points under weak assumptions on $(C_1,\ldots,C_d,T_1,T_2,\ldots)$. In contrast to earlier studies, this includes the most intricate case when the $T_j$ take both positive and negative values with positive probability. In this case, in some situations, the set of fixed points is a subset of the corresponding set when the $T_j$ are replaced by their absolute values, while in other situations, additional solutions arise.

math.PR

Power and exponential moments of the number of visits and related quantities for perturbed random walks

Let $(ξ_1,η_1),(ξ_2,η_2),...$ be a sequence of i.i.d.\ copies of a random vector $(ξ,η)$ taking values in $\R^2$, and let $S_n := ξ_1+...+ξ_n$. The sequence $(S_{n-1} + η_n)_{n \geq 1}$ is then called perturbed random walk. We study random quantities defined in terms of the perturbed random walk: $τ(x)$, the first time the perturbed random walk exits the interval $(-\infty,x]$, $N(x)$, the number of visits to the interval $(-\infty,x]$, and $ρ(x)$, the last time the perturbed random walk visits the interval $(-\infty,x]$. We provide criteria for the a.s.\ finiteness and for the finiteness of exponential moments of these quantities. Further, we provide criteria for the finiteness of power moments of $N(x)$ and $ρ(x)$.

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The functional equation of the smoothing transform

Given a sequence $T=(T_i)_{i\geq1}$ of nonnegative random variables, a function f on the positive halfline can be transformed to $\mathbb{E}\prod_{i\geq1}f(tT_i)$. We study the fixed points of this transform within the class of decreasing functions. By exploiting the intimate relationship with general branching processes, a full description of the set of solutions is established without the moment conditions that figure in earlier studies. Since the class of functions under consideration contains all Laplace transforms of probability distributions on $[0,\infty)$, the results provide the full description of the set of solutions to the fixed-point equation of the smoothing transform, $X\stackrel{d}{=}\sum_{i\geq1}T_iX_i$, where $\stackrel{d}{=}$ denotes equality of the corresponding laws, and $X_1,X_2,...$ is a sequence of i.i.d. copies of X independent of T. Further, since left-continuous survival functions are covered as well, the results also apply to the fixed-point equation $X\stackrel{d}{=}\inf\{X_i/T_i:i\geq1,T_i>0\}$. Moreover, we investigate the phenomenon of endogeny in the context of the smoothing transform and, thereby, solve an open problem posed by Aldous and Bandyopadhyay.

math.PR

Fixed points of the smoothing transform: Two-sided solutions

Given a sequence $(C,T) = (C,T_1,T_2,...)$ of real-valued random variables with $T_j \geq 0$ for all $j \geq 1$ and almost surely finite $N = \sup\{j \geq 1: T_j > 0\}$, the smoothing transform associated with $(C,T)$, defined on the set $\mathcal{P}(\R)$ of probability distributions on the real line, maps an element $P\in\mathcal{P}(\R)$ to the law of $C + \sum_{j \geq 1} T_j X_j$, where $X_1,X_2,...$ is a sequence of i.i.d.\ random variables independent of $(C,T)$ and with distribution $P$. We study the fixed points of the smoothing transform, that is, the solutions to the stochastic fixed-point equation $X_{1}\stackrel{\mathrm{d}}{=}C + \sum_{j \geq 1} T_j X_j$. By drawing on recent work by the authors with J.D.\;Biggins, a full description of the set of solutions is provided under weak assumptions on the sequence $(C,T)$. This solves problems posed by Fill and Janson \cite{FJ2000} and Aldous and Bandyopadhyay \cite{AB2005}. Our results include precise characterizations of the sets of solutions to large classes of stochastic fixed-point equations that appear in the asymptotic analysis of divide-and-conquer algorithms, for instance the \texttt{Quicksort} equation.

math.PR

Fixed points of inhomogeneous smoothing transforms

We consider the inhomogeneous version of the fixed-point equation of the smoothing transformation, that is, the equation $X \stackrel{d}{=} C + \sum_{i \geq 1} T_i X_i$, where $\stackrel{d}{=}$ means equality in distribution, $(C,T_1,T_2,...)$ is a given sequence of non-negative random variables and $X_1,X_2,...$ is a sequence of i.i.d.\ copies of the non-negative random variable $X$ independent of $(C,T_1,T_2,...)$. In this situation, $X$ (or, more precisely, the distribution of $X$) is said to be a fixed point of the (inhomogeneous) smoothing transform. In the present paper, we give a necessary and sufficient condition for the existence of a fixed point. Further, we establish an explicit one-to-one correspondence with the solutions to the corresponding homogeneous equation with C=0. Using this correspondence, we present a full characterization of the set of fixed points under mild assumptions.

math.PR

Exponential moments of first passage times and related quantities for random walks

For a zero-delayed random walk on the real line, let $τ(x)$, $N(x)$ and $ρ(x)$ denote the first passage time into the interval $(x,\infty)$, the number of visits to the interval $(-\infty,x]$ and the last exit time from $(-\infty,x]$, respectively. In the present paper, we provide ultimate criteria for the finiteness of exponential moments of these quantities. Moreover, whenever these moments are finite, we derive their asymptotic behaviour, as $x \to \infty$.

math.PR

A min-type stochastic fixed-point equation related to the smoothing transformation

This paper is devoted to the study of the stochastic fixed-point equation X \stackrel{d}{=} \inf_{i \geq 1: T_i > 0} X_i/T_i and the connection with its additive counterpart $X \stackrel{d}{=} \sum_{i\ge 1}T_{i}X_{i}$ associated with the smoothing transformation. Here $\stackrel{d}{=}$ means equality in distribution, $T := (T_i)_{i \geq 1}$ is a given sequence of nonnegative random variables and $X, X_1, ...$ is a sequence of nonnegative i.i.d. random variables independent of $T$. We draw attention to the question of the existence of nontrivial solutions and, in particular, of special solutions named $α$-regular solutions $(α>0)$. We give a complete answer to the question of when $α$-regular solutions exist and prove that they are always mixtures of Weibull distributions or certain periodic variants. We also give a complete characterization of all fixed points of this kind. A disintegration method which leads to the study of certain multiplicative martingales and a pathwise renewal equation after a suitable transform are the key tools for our analysis. Finally, we provide corresponding results for the fixed points of the related additive equation mentioned above. To some extent, these results have been obtained earlier by Iksanov.

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