em.step {GaussianHMM1d}R Documentation

Function to perform the E-M steps for the estimation of the paramters

Description

This function perform the E-M steps for the estimation of the parameters of a univariate Gaussian HMM.

Usage

em.step(y, mu, sigma, Q)

Arguments

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);

Value

f

values of the density function at time n+k

w

weights of the mixture

Author(s)

Bouchra R Nasri and Bruno N Rémillard, January 31, 2019

References

Chapter 10.2 of B. Rémillard (2013). Statistical Methods for Financial Engineering, Chapman and Hall/CRC Financial Mathematics Series, Taylor & Francis.

Examples

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)

[Package GaussianHMM1d version 1.1.0 Index]