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A model with space-varying regression coefficients for clustering multivariate spatial count data.

Francesco Lagona1,2, Monia Ranalli3, Elisabetta Barbi3

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Summary
This summary is machine-generated.

This study introduces a new model for analyzing spatial count data with unobserved factors. The multivariate hidden Markov field effectively captures varying effects across regions, improving mortality data analysis.

Keywords:
Potts modelcause-specific mortalitycomposite likelihoodhidden Markov fieldmodel-based clustering

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Area of Science:

  • Biostatistics
  • Spatial Statistics
  • Epidemiology

Background:

  • Multivariate spatial count data often exhibit unobserved spatial segmentation.
  • Traditional regression models with constant effects may be inadequate for such data.
  • Cause-specific mortality data analysis requires methods that account for spatial heterogeneity.

Purpose of the Study:

  • To develop a novel statistical model for analyzing multivariate spatial count data with space-varying effects.
  • To address the limitations of space-constant regression models in segmented spatial data.
  • To apply the proposed model to cause-specific mortality data, examining spatial variations in demographic effects.

Main Methods:

  • Proposed a multivariate hidden Markov field model.
  • Utilized Poisson regressions with spatially correlated coefficients driven by a spatial multinomial process.
  • Employed composite likelihood methods for parameter estimation.
  • Developed a parsimonious approach using a finite number of latent classes.

Main Results:

  • The proposed model successfully captured spatial variations in effects.
  • Demonstrated the ability to model multivariate count data with latent classes.
  • The method effectively analyzed spatial variations in gender and age effects in Italian mortality data.

Conclusions:

  • The multivariate hidden Markov field offers a flexible framework for analyzing segmented spatial count data.
  • The developed composite likelihood methods provide efficient parameter estimation.
  • The model enhances the understanding of spatial heterogeneity in epidemiological studies, particularly for mortality data.