Title :
Parameter estimation of Multivariate Geographically Weighted Regression model using matrix laboratory
Author :
Harini, S. ; Purhadi
Abstract :
Model of Multivariate Geographically Weighted Regression (MGWR) is the extension of multivariate spatial liniear model with local observation characters for each observation location. To obtain distribution of the MGWR model, parameter estimation of βh (ui, vi) and variance covariance matrix of error (Σ (ui, vi )) is require to be determined. Besides using mathematical approach, matrix laboratory (MATLAB) algorithm can also be used to obtain parameter estimation of model of MGWR. The MATLAB is a high level programming language base on numerical computing technique to solve problems which involves mathematical operations with array data bases using matrix and vector formulations. Compared to mathematical approach, MATLAB has some advantages which are extensible and no constraint of variable dimension.
Keywords :
covariance matrices; error statistics; high level languages; mathematics computing; parameter estimation; regression analysis; MATLAB algorithm; MGWR model distribution; array databases; error variance covariance matrix; high level programming language; local observation characters; mathematical operations; matrix formulations; matrix laboratory; multivariate geographically weighted regression model; multivariate spatial linear model; numerical computing technique; observation location; parameter estimation; vector formulations; Computational modeling; Data models; MATLAB; Mathematical model; Numerical models; Parameter estimation; Predictive models; MGWR; algorithm; estimation; matrix laboratory; variance-covariance;
Conference_Titel :
Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1581-4
DOI :
10.1109/ICSSBE.2012.6396622