Approximated conditional densities for \(X(t) | X(t_0) = x_0\) of a diffusion process.
dcKessler(x, t, x0, t0, theta, d, dx, dxx, s, sx, sxx,
log=FALSE)
a numeric vector
vector of quantiles.
lag or time.
the value of the process at time t0; see details.
initial time.
parameter of the process; see details.
logical; if TRUE, probabilities \(p\) are given as \(\log(p)\).
drift coefficient as a function; see details.
partial derivative w.r.t. x of the
drift coefficient; see details.
second partial derivative wrt x^2 of the
drift coefficient; see details.
diffusion coefficient as a function; see details.
partial derivative w.r.t. x of the
diffusion coefficient; see details.
second partial derivative w.r.t. x^2 of the
diffusion coefficient; see details.
Stefano Maria Iacus
This function returns the value of the conditional density of
\(X(t) | X(t_0) = x_0\) at point x.
All the functions d, dx, dxx, dt, s, sx,
and sxx must be functions of t, x, and theta.
Kessler, M. (1997) Estimation of an ergodic diffusion from discrete observations, Scand. J. Statist., 24, 211-229.