2 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
Bayesian Semi-Parametric Spatial Dispersed Count Model for Precipitation Analysis
Mahsa Nadifar, Andriette Bekker, Mohammad Arashi +1
The appropriateness of the Poisson model is frequently challenged when examining spatial count data marked by unbalanced distributions, over-dispersion, or under-dispersion. Moreov…
A flexible Bayesian non-confounding spatial model for analysis of dispersed count data in clinical studies
Mahsa Nadifar, Hossein Baghishani, Afshin Fallah
In employing spatial regression models for counts, we usually meet two issues. First, ignoring the inherent collinearity between covariates and the spatial effect would lead to cau…
Flexible Bayesian Modeling of Counts: Constructing Penalized Complexity Priors
Mahsa Nadifar, Hossein Baghishani, Thomas Kneib +1
Many of the data, particularly in medicine and disease mapping are count. Indeed, the under or overdispersion problem in count data distrusts the performance of the classical Poiss…
Statistical modeling of groundwater quality assessment in Iran using a flexible Poisson likelihood
Mahsa Nadifar, Hossein Baghishani, Afshin Fallah +1
Assessing water quality and recognizing its associated risks to human health and the broader environment is undoubtedly essential. Groundwater is widely used to supply water for dr…