On a Linear Functional Mixed Effect Model for Spatial Data
Author(s) -
Roya Nasirzadeh,
Jeorge Mateu,
A. R. Soltani
Publication year - 2019
Publication title -
journal of the iranian statistical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.293
H-Index - 6
eISSN - 2538-189X
pISSN - 1726-4057
DOI - 10.29252/jirss.18.2.115
Subject(s) - random effects model , mathematics , principal component analysis , spatial analysis , generalized linear mixed model , linear model , set (abstract data type) , data set , kriging , mixed model , statistics , random function , random variable , functional principal component analysis , fixed effects model , algorithm , computer science , panel data , medicine , meta analysis , programming language
This paper introduces a functional mixed effect random model to model spatial data. In this model, the spatial locations form the index set, while the contributing effects to the response variable are set as a linear mixture of fixed and random effects. These fixed and random effects are linear combinations of L2 functions and random elements, respectively. However, the corresponding linear factors depend on the spatial location variable. Therefore, we develop estimation procedures to estimate the fixed and random coefficients, using spatial functional principal component analysis. Then, we perform prediction by adapting the functional universal kriging method to our model.
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