dcKessler: Approximated conditional law of a diffusion process by... In sde: Simulation and Inference for Stochastic Differential Equations

Description

Approximated conditional densities for X(t) | X(t0) = x0 of a diffusion process.

Usage

 ```1 2``` ```dcKessler(x, t, x0, t0, theta, d, dx, dxx, s, sx, sxx, log=FALSE) ```

Arguments

 `x` vector of quantiles. `t` lag or time. `x0` the value of the process at time `t0`; see details. `t0` initial time. `theta` parameter of the process; see details. `log` logical; if TRUE, probabilities p are given as log(p). `d` drift coefficient as a function; see details. `dx` partial derivative w.r.t. `x` of the drift coefficient; see details. `dxx` second partial derivative wrt `x^2` of the drift coefficient; see details. `s` diffusion coefficient as a function; see details. `sx` partial derivative w.r.t. `x` of the diffusion coefficient; see details. `sxx` second partial derivative w.r.t. `x^2` of the diffusion coefficient; see details.

Details

This function returns the value of the conditional density of X(t) | X(t0) = x0 at point `x`.

All the functions `d`, `dx`, `dxx`, `dt`, `s`, `sx`, and `sxx` must be functions of `t`, `x`, and `theta`.

Value

 `x` a numeric vector

Author(s)

Stefano Maria Iacus

References

Kessler, M. (1997) Estimation of an ergodic diffusion from discrete observations, Scand. J. Statist., 24, 211-229.

sde documentation built on May 31, 2017, 3:58 a.m.