hhsmm (version 0.4.2)

drobust: pdf of the mixture of the robust emission proposed by Qin et al. (2024)

Description

The probability density function of the robust emission proposed by Qin et al. (2024) for a specified observation vector, a specified state and a specified model's parameters

Usage

drobust(x, j, model, control = list(k = 1.345))

Value

the probability density function value

Arguments

x

an observation vector or matrix

j

a specified state between 1 to nstate

model

a hhsmmspec model

control

a list containing the control parameter k with the default value equal to 1.345

Author

Morteza Amini,

References

Qin, S., Tan, Z., and Wu, Y. (2024). On robust estimation of hidden semi-Markov regime-switching models. Annals of Operations Research, 1-33.

Examples

J <- 3
initial <- c(1, 0, 0)
semi <- c(FALSE, TRUE, FALSE)
P <- matrix(c(0.8, 0.1, 0.1, 0.5, 0, 0.5, 0.1, 0.2, 0.7), 
nrow = J, byrow = TRUE)
par <- list(mu = list(list(7, 8),list(10, 9, 11), list(12, 14)),
sigma = list(list(3.8, 4.9), list(4.3, 4.2, 5.4), list(4.5, 6.1)),
mix.p = list(c(0.3, 0.7), c(0.2, 0.3, 0.5), c(0.5, 0.5)))
sojourn <- list(shape = c(0, 3, 0), scale = c(0, 10, 0), type = "gamma")
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixmvnorm, sojourn = sojourn, semi = semi)
train <- simulate(model, nsim = c(10, 8, 8, 18), seed = 1234, 
remission = rmixmvnorm)
mu = list(0,1)
sigma = list(1,2)
robustmodel = list(parms.emission = list(mu = mu,sigma = sigma))
p = drobust(train$x, 1, robustmodel)