158 research outputs found

    Jump-type Hunt processes generated by lower bounded semi-Dirichlet forms

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    Let EE be a locally compact separable metric space and mm be a positive Radon measure on it. Given a nonnegative function kk defined on E×EE\times E off the diagonal whose anti-symmetric part is assumed to be less singular than the symmetric part, we construct an associated regular lower bounded semi-Dirichlet form η\eta on L2(E;m)L^2(E;m) producing a Hunt process X0X^0 on EE whose jump behaviours are governed by kk. For an arbitrary open subset DED\subset E, we also construct a Hunt process XD,0X^{D,0} on DD in an analogous manner. When DD is relatively compact, we show that XD,0X^{D,0} is censored in the sense that it admits no killing inside DD and killed only when the path approaches to the boundary. When EE is a dd-dimensional Euclidean space and mm is the Lebesgue measure, a typical example of X0X^0 is the stable-like process that will be also identified with the solution of a martingale problem up to an η\eta-polar set of starting points. Approachability to the boundary D\partial D in finite time of its censored process XD,0X^{D,0} on a bounded open subset DD will be examined in terms of the polarity of D\partial D for the symmetric stable processes with indices that bound the variable exponent α(x)\alpha(x).Comment: Published in at http://dx.doi.org/10.1214/10-AOP633 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

    On unique extension of time changed reflecting Brownian motions

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    Let DD be an unbounded domain in \RR^d with d3d\geq 3. We show that if DD contains an unbounded uniform domain, then the symmetric reflecting Brownian motion (RBM) on D\overline D is transient. Next assume that RBM XX on D\overline D is transient and let YY be its time change by Revuz measure 1D(x)m(x)dx{\bf 1}_D(x) m(x)dx for a strictly positive continuous integrable function mm on D\overline D. We further show that if there is some r>0r>0 so that DB(0,r)D\setminus \overline {B(0, r)} is an unbounded uniform domain, then YY admits one and only one symmetric diffusion that genuinely extends it and admits no killings. In other words, in this case XX (or equivalently, YY) has a unique Martin boundary point at infinity.Comment: To appear in Ann. Inst. Henri Poincare Probab. Statis

    Time changes of symmetric diffusions and Feller measures

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    We extend the classical Douglas integral, which expresses the Dirichlet integral of a harmonic function on the unit disk in terms of its value on boundary, to the case of conservative symmetric diffusion in terms of Feller measure, by using the approach of time change of Markov processes.Comment: Published at http://dx.doi.org/10.1214/009117904000000649 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

    On the Works of Hiroshi Kunita in the Sixties

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    Traces of symmetric Markov processes and their characterizations

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    Time change is one of the most basic and very useful transformations for Markov processes. The time changed process can also be regarded as the trace of the original process on the support of the Revuz measure used in the time change. In this paper we give a complete characterization of time changed processes of an arbitrary symmetric Markov process, in terms of the Beurling--Deny decomposition of their associated Dirichlet forms and of Feller measures of the process. In particular, we determine the jumping and killing measure (or, equivalently, the L\'{e}vy system) for the time-changed process. We further discuss when the trace Dirichlet form for the time changed process can be characterized as the space of finite Douglas integrals defined by Feller measures. Finally, we give a probabilistic characterization of Feller measures in terms of the excursions of the base process.Comment: Published at http://dx.doi.org/10.1214/009117905000000657 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org
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