em.step {GaussianHMM1d} | R Documentation |
This function perform the E-M steps for the estimation of the parameters of a univariate Gaussian HMM.
em.step(y, mu, sigma, Q)
y |
points at which the density function is comptuted (mx1); |
mu |
vector of means for each regime (r x 1); |
sigma |
vector of standard deviations for each regime (r x 1); |
Q |
transition probality matrix (r x r); |
f |
values of the density function at time n+k |
w |
weights of the mixture |
Bouchra R Nasri and Bruno N Rémillard, January 31, 2019
Chapter 10.2 of B. Rémillard (2013). Statistical Methods for Financial Engineering, Chapman and Hall/CRC Financial Mathematics Series, Taylor & Francis.
mu <- c(-0.3 ,0.7) ; sigma <- c(0.15,0.05); Q <- matrix(c(0.8, 0.3, 0.2, 0.7),2,2) ;
data <- Sim.HMM.Gaussian.1d(mu,sigma,Q,eta0=1,100)$x
out <- em.step(data,mu,sigma,Q)