102 research outputs found
An optimal stopping problem with finite horizon for sums of I.I.D. random variables
AbstractThe problem of selling a commodity optimally at one of n successive time instants leads to the optimal stopping problem for the finite sequence ((n−j)lSj)1⩽j⩽n, where Sj=U1 + … + Uj, U1, U2,… are i.i.d., E(U1) = 0 and E(U21) = 1. The optimal stopping time πn is seen to be of the form τn = inf{j|j = n or j < n, Sj⩾clj,n}, where c1j,1>…>cln−1,n = 0 satisfyn−12 cj,nl → αl(1 − t)11, if n → ∞, j/n →t ṫ[0,1]. αl > 0 is the solution of the equation d2l+2dx2l+2(Ф/φ)(α) = (α + α−1)d2l+2dx2l+2(Ф/φ)(α). For the value vln we have n−32vnl → vl. vl is explicitly computed. In the normal case we also obtain results on the speed of convergence of n−12cj,nl and n−32vnl
On the small-time behavior of subordinators
We prove several results on the behavior near t=0 of for certain
-valued stochastic processes . In particular, we show
for L\'{e}vy subordinators that the Pareto law on is the only
possible weak limit and provide necessary and sufficient conditions for the
convergence. More generally, we also consider the weak convergence of
as for a decreasing function that is slowly varying at zero.
Various examples demonstrating the applicability of the results are presented.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ363 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Joint Distributions of the Numbers of Visits for Finite-State Markov Chains
AbstractFor a discrete-time Markov chain with finite state space {1, …, r} we consider the joint distribution of the numbers of visits in states 1, …, r−1 during the firstNsteps or before theNth visit tor. From the explicit expressions for the corresponding generating functions we obtain the limiting multivariate distributions asN→∞ when staterbecomes asymptotically absorbing and forj=1, …, r−1 the probability of a transition fromrtojis of order 1/N
- …