Although the application of reproducing kernel has been explored in different fields in the
past twenty to thirty years and the relevant researches are active in the recent five years …

We derive rates of contraction of posterior distributions on nonparametric or semiparametric
models based on Gaussian processes. The rate of contraction is shown to depend on the …

In recent years, there has been great interest in the simulation of long-range dependent
processes, in particular fractional Brownian motion. Motivated by applications in …

A paper that studies two types of integral transformation associated with fractional Brownian
motion. They are applied to construct approximation schemes for fractional Brownian motion …

A new method by the reproducing kernel Hilbert space is applied to an inverse heat problem
of determining a time-dependent source parameter. The problem is reduced to a system of …

Volterra processes are continuous stochastic processes whose covariance function can be
written in the form R (s, t)=∫ 0 s∧ t K (s, r) K (t, r) dr, where K is a suitable square integrable …

We study fractional stochastic volatility models in which the volatility process is a positive
continuous function σ of a continuous Gaussian process B. Forde and Zhang established a …

By making a seminal use of the maximum modulus principle of holomorphic functions we
prove existence of n-best kernel approximation for a wide class of reproducing kernel Hilbert …

We construct the “expected signature matching” estimator for differential equations driven by
rough paths and we prove its consistency and asymptotic normality. We use it to estimate …

A concept of volatility modulated Volterra processes is introduced. Apart from some brief
discussion of generalities, the paper focusses on the special case of backward moving …