Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistency of the maximum likelihood estimator in seasonal hidden
  Markov models   In this paper, we introduce a variant of hidden Markov models in which the
transition probabilities between the states, as well as the emission
distributions, are not constant in time but vary in a periodic manner. This
class of models, that we will call seasonal hidden Markov models (SHMM) is
particularly useful in practice, as many applications involve a seasonal
behaviour. However, up to now, there is no theoretical result regarding this
kind of model. We show that under mild assumptions, SHMM are identifiable: we
can identify the transition matrices and the emission distributions from the
joint distribution of the observations on a period, up to state labelling. We
also give sufficient conditions for the strong consistency of the maximum
likelihood estimator (MLE). These results are applied to simulated data, using
the EM algorithm to compute the MLE. Finally, we show how SHMM can be used in
real world applications by applying our model to precipitation data, with
mixtures of exponential distributions as emission distributions.

33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph