Mathematics – Probability
Scientific paper
2007-02-28
Annals of Probability 2006, Vol. 34, No. 6, 2406-2440
Mathematics
Probability
Published at http://dx.doi.org/10.1214/009117906000000458 in the Annals of Probability (http://www.imstat.org/aop/) by the Ins
Scientific paper
10.1214/009117906000000458
We give lower bounds for the density $p_T(x,y)$ of the law of $X_t$, the solution of $dX_t=\sigma (X_t) dB_t+b(X_t) dt,X_0=x,$ under the following local ellipticity hypothesis: there exists a deterministic differentiable curve $x_t, 0\leq t\leq T$, such that $x_0=x, x_T=y$ and $\sigma \sigma ^*(x_t)>0,$ for all $t\in \lbrack 0,T].$ The lower bound is expressed in terms of a distance related to the skeleton of the diffusion process. This distance appears when we optimize over all the curves which verify the above ellipticity assumption. The arguments which lead to the above result work in a general context which includes a large class of Wiener functionals, for example, It\^{o} processes. Our starting point is work of Kohatsu-Higa which presents a general framework including stochastic PDE's.
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