MCEM_POLIO: Monte Carlo EM algorithm - Shorterned for the Paper example

In hlennon/copulaIVTS: Dependence modelling of a digitised Gaussian ARMA(p,q) copula model for Integer-Valued Time Series

Description

Use: Computes the MLEs of the Gaussian copula model using the Monte Carlo EM algorithm

Usage

MCEM_POLIO(obs.data, Monte_Carlo_samples, p, q, initial,  num_iterations,
                prec=0.01,  marginal_dist="negbin",
                optim_method="nmkb", compute_stderrors=TRUE)

Arguments

obs.data	Observed Data
Monte_Carlo_samples	The number of Monte Carlo samples, m, to approximate the conditional expectation, if m is a vector it repeats num_iterations with each numbr in the vector
p	order of the ar parameters in ARMA(p,q)
q	order of the ma parameters in ARMA(p,q)
initial	initial parameter values for the EM algorithm
prec	precision for stopping criteria
num_iterations	Number of iterations
marginal_dist	Marginal distributions
optim_method	Default is "nmkb" from the R package dfoptim, however "CG", "BFGS" or "nlm" can be used. optimx() from the optimx R package suggests all 4 can be used but in practice only "L-BFGS-B" have provided a good optimisation routine
compute_stderrors	TRUE or FALSE

Value

Iterations for the MCEM algorithm which converge to the MLE estimates of the ARMA(1,1) parameters

Author(s)

Hannah Lennon <[e-mail]>

References

Lennon H., & Yuan J., Estimation of a digitised Gaussian ARMA model by Monte Carlo Expectation Maximisation, Computational Statistics & Data Analysis 2018;8:e020683

Lennon, H., 2016. Gaussian copula modelling for integer-valued time series (Doctoral Thesis, University of Manchester).

See Also

First and Second Year Transfer Reports

Examples

library(gamlss.data)
data(polio)
# MCEM_POLIO(obs.data=polio, Monte_Carlo_samples=10, p=1, q=1, initial=c(0,0), marginal_dist="negbin", num_iterations=10)

hlennon/copulaIVTS documentation built on Dec. 20, 2021, 4:45 p.m.