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492 – Application of Nonparametric Methods
Spatio-Temporal Single Index Models for Correlated Data
Hamdy F. F Mahmoud
Virginia Tech; Assiut University
Modeling spatially-temporally correlated data using parametric models is common, however semiparametric models are very limited in this area. This article introduces a semiparametric spatialtemporal effects model, in which spatial effects are integrated into the single index function and temporal effects are additive to the single index function. We refer to this model as "semiparametric nonadditive spatio-temporal single index model� (NST-SIM). For estimation, Monte Carlo Expectation Maximization based algorithm is used. NST-SIM has many advantages demonstrated by simulation studies and real data application. It has smaller mean square error and higher accurate prediction compared to non-integrated spatial temporal single index model. It is applied to South Korean mortality data of six major cities and interesting results are found.