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We study limit theorems for time-dependent averages of the form $X_t:=frac{1}{2L(t)}int_{-L(t)}^{L(t)} u(t, x) , dx$, as $tto infty$, where $L(t)=exp(lambda t)$ and $u(t, x)$ is the solution to a stochastic heat equation on $mathbb{R}_+times mathbb{R}$ driven by space-time white noise with $u_0(x)=1$ for all $xin mathbb{R}$. We show that for $X_t$ (i) the weak law of large numbers holds when $lambda>lambda_1$, (ii) the strong law of large numbers holds when $lambda>lambda_2$, (iii) the central limit theorem holds when $lambda>lambda_3$, but fails when $lambda <lambda_4leq lambda_3$, (iv) the quantitative central limit theorem holds when $lambda>lambda_5$, where $lambda_i$s are positive constants depending on the moment Lyapunov exponents of $u(t, x)$.
Suppose that ${u(t,, x)}_{t >0, x inmathbb{R}^d}$ is the solution to a $d$-dimensional stochastic heat equation driven by a Gaussian noise that is white in time and has a spatially homogeneous covariance that satisfies Dalangs condition. The purpose
Let ${u(t,,x)}_{tge 0, xin mathbb{R}^d}$ denote the solution of a $d$-dimensional nonlinear stochastic heat equation that is driven by a Gaussian noise, white in time with a homogeneous spatial covariance that is a finite Borel measure $f$ and satisf
The approximation of integral type functionals is studied for discrete observations of a continuous It^o semimartingale. Based on novel approximations in the Fourier domain, central limit theorems are proved for $L^2$-Sobolev functions with fractiona
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