multivariate poisson hidden markov models for analysis of spatial ...

consequently lower herbicide costs, timesaving because of fewer stops to refill, less
non-target spraying, which reduces potential environmental risks. Further, this model
may lead researchers to find other factors, such as soil moisture and fertilizer levels, to
determine the states.
The modified EM algorithm for the multivariate Poisson distribution was used to
estimate the parameters. There are some problems with the EM algorithm for this
model, such as the non-convergence to global optimum and slow convergence.
Convergence and the properties of convergence depend heavily on the starting values.
Therefore, further studies can be focused on the different optimization techniques for
the multivariate Poisson hidden Markov model.
Another disadvantage of the proposed modeling is the lack of availability of software.
The modelling of this multivariate Poisson hidden Markov model cannot be done in a
user friendly way and one has to write their own code to solve the problem. However,
now on, the public can use our Splus/R codes for the analysis of the multivariate
Poisson hidden Markov model. Also for the small datasets, this model may not provide
the better estimates, since the most of the hidden Markov model properties proved
under the assumption of asymptotic behavior.
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