Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 492 - Application of Nonparametric Methods
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Nonparametric Statistics
Abstract #312397
Title: Spatio-Temporal Single Index Models for Correlated Data
Author(s): Hamdy Mahmoud* and Inyoung Kim
Companies: Virginia Tech and Virginia Tech
Keywords: MCEM algorithm; mixed model; single index model; spatio-temporal data
Abstract:

Modeling spatially-temporally correlated data using parametric models is common, however semiparametric models are very limited in this area. This article introduces a semiparametric spatial-temporal 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 non additive 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.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program