OptimMCL.HCAR

A list or an environment contains the variables same as described in <code><a rd-options="" href="/link/sim.HCAR?package=mclcar&version=0.2-0" data-mini-rdoc="mclcar::sim.HCAR">sim.HCAR</a></code>.

data

Starting value for the importance sampler parameter same as described in <code><a rd-options="" href="/link/sim.HCAR?package=mclcar&version=0.2-0" data-mini-rdoc="mclcar::sim.HCAR">sim.HCAR</a></code>.

psi0

a list of tuning parameters to control the algorithm. Details to be found at <code><a rd-options="" href="/link/OptimMCL?package=mclcar&version=0.2-0" data-mini-rdoc="mclcar::OptimMCL">OptimMCL</a></code>

control

The function uses an iterative procedure of directly maximising the
Monte Carlo likelihood of a hierechical conditional auto-regressive model 
and the updating step size is limited by defining
an experimental region using the estimated Monte Carlo variance.

The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) <doi:10.1016/j.spasta.2017.01.002>.

Zhe Sha

mclcar

Estimating Conditional Auto-Regressive (CAR) Models using Monte
Carlo Likelihood Methods

OptimMCL.HCAR function

A list or an environment contains the variables same as described in <code><a rd-options='' href='sim.HCAR'>sim.HCAR</a></code>.

Starting value for the importance sampler parameter same as described in <code><a rd-options='' href='sim.HCAR'>sim.HCAR</a></code>.

a list of tuning parameters to control the algorithm. Details to be found at <code><a rd-options='' href='OptimMCL'>OptimMCL</a></code>

OptimMCL.HCAR: Iterative procedure for maximising the Monte Carlo likelihood of the hierachical conditional auto-regressive models.

Description

Usage

Arguments

Value

See Also