First-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model

Tabandeh, Atefeh; Ghodsi, Alireza

doi:10.1080/03610918.2022.2083164

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…$$ Thus, the cross‐correlation between the observation

{Y}_{ij}

and the error term

{\epsilon}_{ij}

has a complicated structure in the multilateral model as compared to the simple unilateral spatial auto‐regressive process in Ghodsi et al (2012), Ghodsi (2015), Pereira Sassi and Paraiba (2023) and besides these covariances are difficult to obtain. See also the extension in Tabandeh and Ghodsi (2022).…”

Section: The New Modelmentioning

confidence: 99%

See 1 more Smart Citation

The multilateral spatial integer‐valued process of order 1

Karlis

Chutoo

Khan

et al. 2023

Statistica Neerlandica

View full text Add to dashboard Cite

In spatial count data analysis, modeling with a multilateral lattice structure presents some important challenges. They include both the model construction and the estimation of the model parameters, since the structure accommodates the left, right, top, bottom, and diagonal site effects. Thus, the multilateral spatial process unifies all the popular spatial subclasses that include the unilateral, Rook, Bishop, and Queen models and, hence, makes it suitable for a wide variety of applications. This paper introduces a first‐order multilateral integer‐valued spatial process, based on a binomial thinning mechanism and some innovation term, under both stationary and nonstationary conditions. The estimation of parameters is handled by the conditional maximum likelihood estimation (CML) approach. Simulation experiments are implemented to assess the consistency of the CML estimators in the stationary and nonstationary multilateral spatial model and its subclasses, based on different grid sizes and under both covariate and noncovariate designs. The proposed model, along with its subclasses are applied to real datasets.

show abstract

“…$$ Thus, the cross‐correlation between the observation

{Y}_{ij}

and the error term

{\epsilon}_{ij}

Section: The New Modelmentioning

confidence: 99%

“…and besides these covariances are difficult to obtain. See also the extension in Tabandeh and Ghodsi (2022). Also note that while we defined the model in its general version, we may select a more parsimonious version by assuming less parameters.…”

Section: The New Modelmentioning

confidence: 99%