Spatial Weight Determination of GSTAR(1;1) Model by Using Kernel Function

The stochastic process models with the index parameters such as time and location were investigated in this paper. The model used was GSTAR (1;1), and it was applied to the Gamma ray log data. The important thing to be assessed in this model is the determination of the space weight matrix. Commonly,...

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Published inJournal of physics. Conference series Vol. 1028; no. 1; pp. 12223 - 12230
Main Authors Yundari, Pasaribu, U S, Mukhaiyar, U, Heriawan, M N
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.06.2018
Subjects
Covariance matrix
Euclidean geometry
Gamma rays
Kernel functions
Physics
Stochastic processes
Weighting functions
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Summary:The stochastic process models with the index parameters such as time and location were investigated in this paper. The model used was GSTAR (1;1), and it was applied to the Gamma ray log data. The important thing to be assessed in this model is the determination of the space weight matrix. Commonly, the spatial weight matrix was determined based on the Euclidean distance, but not based on data. In this work, we use the kernel function approach to determine the spatial weighting function whose domain was in the form of data observation. In addition, we also study the influence of this weight matrix to the stationary condition of GSTAR (1;1) model, and we use the inverse of auto-covariance matrices or IAcM methods. The results showed that the kernel weights matrix approach still being met influence on stationary of this model.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1028/1/012223