|
Activity Number:
|
253
- Contributed Poster Presentations: Section on Statistical Computing
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, July 30, 2018 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Computing
|
|
Abstract #330189
|
|
|
Title:
|
Estimation of Space-Time ARMAX Model
|
|
Author(s):
|
Dongping Fang*
|
|
Companies:
|
Zurich
|
|
Keywords:
|
time-space; ARMAX; Kalman flter; missing values; big data
|
|
Abstract:
|
Space-time Autoregressive-moving-average model with exogenous inputs (STARMAX) model models how a time-space target process depends on its own and its neighbors' past, and on other time-space exogenous inputs. Time-space data arise in many physical science studies. The interpretability of STARMAX model makes it highly desirable. There is not a ready to use program that will fit this model. This paper extends the model by introducing the similar time-space lag structure to exogenous inputs. This paper also develops and implements a fast Kalman filter parameter estimation and prediction procedure that allows missing values in target process, and can be applied on big data.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2018 program
|
