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Peter Kern

Publications and source records attributed to Peter Kern.

27 records · Page 2Linked to original sources
arXiv2014

A link between Bougerol's identity and a formula due to Donati-Martin, Matsumoto and Yor

We point out an easy link between two striking identities on exponential functionals of the Wiener process and the Wiener bridge originated by Bougerol, and Donati-Martin, Matsumoto and Yor, respectively. The link is established using a continuous one-parameter family of Gaussian processes known as $α$-Wiener bridges or scaled Wiener bridges, which in case $α=0$ coincides with a Wiener process and for $α=1$ is a version of the Wiener bridge.

math.PR↗
arXiv2013

The dimension of the St. Petersburg game

Let $S_n$ be the total gain in $n$ repeated St.\ Petersburg games. It is known that $n^{-1}(S_n-n\log_2n)$ converges in distribution to a random element $Y(t)$ along subsequences of the form $k(n)=2^{p(n)}t(n)$ with $p(n)=\lceil\log_2k(n)\rceil\to\infty$ and $t(n)\to t\in[\frac12,1]$. We determine the Hausdorff and box-counting dimension of the range and the graph for almost all sample paths of the stochastic process $\{Y(t)\}_{t\in[1/2,1]}$. The results are compared to the fractal dimension of the corresponding limiting objects when gains are given by a deterministic sequence initiated by Hugo Steinhaus.

math.PR↗
arXiv2012

Scaling limits of coupled continuous time random walks and residual order statistics through marked point processes

A continuous time random walk (CTRW) is a random walk in which both spatial changes represented by jumps and waiting times between the jumps are random. The CTRW is coupled if a jump and its preceding or following waiting time are dependent random variables, respectively. The aim of this paper is to explain the occurrence of different limit processes for CTRWs with forward- or backward-coupling in Straka and Henry (2011) using marked point processes. We also establish a series representation for the different limits. The methods used also allow us to solve an open problem concerning residual order statistics by LePage (1981).

math.PR↗
arXiv2012

A general multiparameter version of Gnedenko's transfer theorem

Limit theorems for a random number of independent random variables are frequently called transfer theorems. Investigations into this direction for sums of random variables with independent random sample size have been originated by Gnedenko. We present a widely applicable transfer theorem for random variables on a general metric space with random multiparameters instead of random sample sizes. This summarizes an intrinsic principle behind the transfer type results known from the literature.

math.PR↗
arXiv2012

Hausdorff dimension of operator semistable Lévy processes

Let $X=\{X(t)\}_{t\geq0}$ be an operator semistable Lévy process in $\rd$ with exponent $E$, where $E$ is an invertible linear operator on $\rd$ and $X$ is semi-selfsimilar with respect to $E$. By refining arguments given in Meerschaert and Xiao \cite{MX} for the special case of an operator stable (selfsimilar) Lévy process, for an arbitrary Borel set $B\subseteq\rr_+$ we determine the Hausdorff dimension of the partial range $X(B)$ in terms of the real parts of the eigenvalues of $E$ and the Hausdorff dimension of $B$.

math.PR↗
arXiv2011

Sample path deviations of the Wiener and the Ornstein-Uhlenbeck process from its bridges

We study sample path deviations of the Wiener process from three different representations of its bridge: anticipative version, integral representation and space-time transform. Although these representations of the Wiener bridge are equal in law, their sample path behavior is quite different. Our results nicely demonstrate this fact. We calculate and compare the expected absolute, quadratic and conditional quadratic path deviations of the different representations of the Wiener bridge from the original Wiener process. It is further shown that the presented qualitative behavior of sample path deviations is not restricted only to the Wiener process and its bridges. Sample path deviations of the Ornstein-Uhlenbeck process from its bridge versions are also considered and we give some quantitative answers also in this case.

math.PR↗
arXiv2011

General alpha-Wiener bridges

An alpha-Wiener bridge is a one-parameter generalization of the usual Wiener bridge, where the parameter alpha>0 represents a mean reversion force to zero. We generalize the notion of alpha-Wiener bridges to continuous functions $α:[0,T)\to R$. We show that if the limit $\lim_{t\uparrow T}α(t)$ exists and is positive, then a general alpha-Wiener bridge is in fact a bridge in the sense that it converges to 0 at time T with probability one. Further, under the condition $\lim_{t\uparrow T}α(t)\ne 1$ we show that the law of the general alpha-Wiener bridge can not coincide with the law of any non time-homogeneous Ornstein-Uhlenbeck type bridge. In case $\lim_{t\uparrow T}α(t)=1$ we determine all the Ornstein-Uhlenbeck type processes from which one can derive the general alpha-Wiener bridge by conditioning the original Ornstein-Uhlenbeck type process to be in zero at time T.

math.PR↗
arXiv2010

Representations of multidimensional linear process bridges

We derive bridges from general multidimensional linear non time-homogeneous processes using only the transition densities of the original process giving their integral representations (in terms of a standard Wiener process) and so-called anticipative representations. We derive a stochastic differential equation satisfied by the integral representation and we prove a usual conditioning property for general multidimensional linear process bridges. We specialize our results for the one-dimensional case; especially, we study one-dimensional Ornstein-Uhlenbeck bridges.

math.PR↗