The unilateral spatial autoregressive process for the regular lattice two-dimensional spatial discrete data
2021
This paper proposes a generalized framework to analyze spatial count data under a unilateral
regular lattice structure based on thinning type models. We start from the simple spatial integervalued auto-regressive model of order 1. We extend this model in certain directions. First, we
consider various distributions as choices for the innovation distribution to allow for additional
overdispersion. Second, we allow for use of covariate information, leading to a non-stationary
model. Finally, we derive and use other models related to this simple one by considering simplification on the existing model. Inference is based on conditional maximum likelihood approach. We
provide simulation results under different scenarios to understand the behaviour of the conditional
maximum likelihood. A real data application is also provided. Remarks on how the results extend
to other families of models are also given.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
0
Citations
NaN
KQI