An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
hub
Journal of the American Statistical Association 70(350):320--328
2 Pith papers cite this work, alongside 2,470 external citations. Polarity classification is still indexing.
2
Pith papers citing it
2,470
external citations · external index
hub tools
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.
citing papers explorer
-
glmSTARMA -- An R-Package for fitting autoregressive spatio-temporal models following generalized linear models
An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
-
Cross-validation-based optimal feature selection for linear SVM classification
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.