We obtain upper and lower Gaussian-type bounds on the density of each component $Y^i_t$ of the solution $Y_t$ to a multidimensional non-Markovian backward SDE. Our approach is based on the Nourdin-Viens formula and a stochastic version of Wazewski's theorem on the positivity of the components of a solution to an ODE. Furthermore, we apply our results to stochastic gene expression; namely, we estimate the density of the law of the amount of protein generated by a gene in a gene regulatory network.Comment: Version accepted in Potential Analysi
AbstractWe consider a random variable X satisfying almost-sure conditions involving G:=〈DX,−DL−1X〉 w...
summary:Stochastic Riccati equation is a backward stochastic differential equation with singular gen...
The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting wher...
International audienceIn this paper, we derive sufficient conditions for each component of the solut...
The aim of this paper is twofold. Firstly, we derive upper and lower non- Gaussian bounds for the de...
In this paper, we derive sufficient conditions for each component of the solution to a general backw...
27 pagesIn this paper we study upper bounds for the density of solution of stochastic differential e...
AbstractIn this paper we establish lower and upper Gaussian bounds for the solutions to the heat and...
We obtain two-sided bounds for the density of stochastic processes satisfying a weak H"ormander cond...
In this paper, we initiate the study of backward doubly stochastic differential equations (BDSDEs, f...
© 2016, The Author(s). A statistical application to Gene Set Enrichment Analysis implies calculating...
1 figureIn this paper we obtain Gaussian type lower bounds for the density of solutions to stochasti...
In this article, we introduce a backward method to model stochastic gene expression and protein-leve...
We prove the existence of maximal (and minimal) solution for one-dimensional generalized doubly refl...
Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian densi...
AbstractWe consider a random variable X satisfying almost-sure conditions involving G:=〈DX,−DL−1X〉 w...
summary:Stochastic Riccati equation is a backward stochastic differential equation with singular gen...
The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting wher...
International audienceIn this paper, we derive sufficient conditions for each component of the solut...
The aim of this paper is twofold. Firstly, we derive upper and lower non- Gaussian bounds for the de...
In this paper, we derive sufficient conditions for each component of the solution to a general backw...
27 pagesIn this paper we study upper bounds for the density of solution of stochastic differential e...
AbstractIn this paper we establish lower and upper Gaussian bounds for the solutions to the heat and...
We obtain two-sided bounds for the density of stochastic processes satisfying a weak H"ormander cond...
In this paper, we initiate the study of backward doubly stochastic differential equations (BDSDEs, f...
© 2016, The Author(s). A statistical application to Gene Set Enrichment Analysis implies calculating...
1 figureIn this paper we obtain Gaussian type lower bounds for the density of solutions to stochasti...
In this article, we introduce a backward method to model stochastic gene expression and protein-leve...
We prove the existence of maximal (and minimal) solution for one-dimensional generalized doubly refl...
Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian densi...
AbstractWe consider a random variable X satisfying almost-sure conditions involving G:=〈DX,−DL−1X〉 w...
summary:Stochastic Riccati equation is a backward stochastic differential equation with singular gen...
The classical Gaussian concentration inequality for Lipschitz functions is adapted to a setting wher...