estimate,Merton-method {BaPreStoPro} | R Documentation |
Bayesian estimation of a stochastic process Y_t = y_0 \exp( φ t - γ^2/2 t+γ W_t + \log(1+θ) N_t).
## S4 method for signature 'Merton' estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE, proposal = c("normal", "lognormal"), it.xi = 10)
model.class |
class of the jump diffusion model including all required information, see |
t |
vector of time points |
data |
vector of observation variables |
nMCMC |
length of Markov chain |
propSd |
vector of proposal variances for ξ |
adapt |
if TRUE (default), proposal variance is adapted |
proposal |
proposal density for xi: "normal" (default) or "lognormal" |
it.xi |
number of iterations for MH step for ξ inside the Gibbs sampler |
Hermann, S. and F. Ruggeri (2016). Modelling Wear Degradation in Cylinder Liners. SFB 823 discussion paper 06/16.
Hermann, S., K. Ickstadt and C. H. Mueller (2015). Bayesian Prediction for a Jump Diffusion Process with Application to Crack Growth in Fatigue Experiments. SFB 823 discussion paper 30/15.
model <- set.to.class("Merton", parameter = list(thetaT = 0.1, phi = 0.05, gamma2 = 0.1, xi = 10)) t <- seq(0, 1, by = 0.01) data <- simulate(model, t = t, y0 = 0.5, plot.series = TRUE) est <- estimate(model, t, data, 1000) plot(est) ## Not run: est_hidden <- estimate(model, t, data$Y, 1000) plot(est_hidden) ## End(Not run)