Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inference for nonstationary time series of counts with application to
  change-point problems   We consider an integer-valued time series $Y=(Y_t)_{t\in\Z}$ where the models
after a time $k^*$ is Poisson autoregressive with the conditional mean that
depends on a parameter $\theta^*\in\Theta\subset\R^d$. The structure of the
process before $k^*$ is unknown;? it could be any other integer-valued time
series, that is, the process $Y$ could be nonstationary.? It is established
that the maximum likelihood estimator of $\theta^*$ computed on the
nonstationary observations is consistent and asymptotically normal. Next, we
carry out the sequential change-point detection in a large class of Poisson
autoregressive models. We propose a monitoring scheme for detecting change in
the model. The procedure is based on an updated estimator which is computed
without the historical observations. The asymptotic behavior of the detector is
studied, in particular, the above result on the inference in a nonstationary
setting are applied to prove that the proposed procedure is consistent. A
simulation study as well as a real data application are provided.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con