Open AccessPoisson GSTAR model: spatial temporal modeling count data follow generalized linear model and count time series models
Author(s) -
Laksmi Prita Wardhani,
Setiawan Setiawan,
Suhartono Suhartono,
Heri Kuswanto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1490/1/012010
Subject(s) - count data , autoregressive model , series (stratigraphy) , poisson distribution , generalized linear model , mathematics , overdispersion , statistics , star model , quasi likelihood , time series , statistical model , linear model , autoregressive integrated moving average , paleontology , biology
This paper discusses the formation of one temporal spatial model, the Generalized Space Time Autoregressive (GSTAR) model, if the data of model is count data. The development of the GSTAR model is an update or problem completion of the count data which tends to be stationary and non-normal / Gaussian data, because GSTAR model is assumed normal distributed and stationarity. GSTAR modelling for count data refers to the Time series model for count data, which are the Generalized Autoregressive Moving Average (GARMA) model and modeling Count Time Series which has the Generalized Linear Model (GLM) concept. The model formed is called the Poisson GSTAR model