ESTIMATING FUZZY PARAMETERS OF SPATIAL REGRESSION BY ST-DECOMPOSITION METHOD
- Authors
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Asma Othman Hassan
University of Kirkuk, College of Administration and Economics, Department of Statistics -
Jaufar Mosa Mohammed
University of Kirkuk, College of Administration and Economics, Department of Statistics
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- Keywords:
- Fuzzy Spatial Regression FSLX Model ST-Decomposition Method Symmetric Triangular Fuzzy Numbers Spatial Autocorrelation
- Abstract
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This study evaluates the Fuzzy Spatially Lagged Exogenous (FSLX) model for analyzing ambiguous spatial data, featuring a symmetric triangular fuzzy response variable, deterministic crisp explanatory variables, and symmetric triangular fuzzy parameters, estimated via the Symmetric Triangular ST-decomposition matrix method. The model was empirically applied to 2018 traffic fatality data and its contributing factors across six Iraqi governorates. Results demonstrate the efficacy of the ST-decomposition approach in disassembling the fuzzy system into core and spread components to simplify estimation. A statistically significant positive relationship was identified between fatalities and rollover/pedestrian accidents, with Moran's I test confirming significant spatial autocorrelation. Furthermore, spatial lag parameters revealed varying geographical spillover effects among neighboring governorates, maintaining parameter stability and effectively encapsulating observational uncertainty within the fuzzy bounds. Future extensions to spatio-temporal and advanced fuzzy spatial models are recommended.
- References
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