Parameter Estimation of Multivariate Geographically Weighted Regression Model Using Matrix Laboratory

Harini, Sri (2012) Parameter Estimation of Multivariate Geographically Weighted Regression Model Using Matrix Laboratory. Presented at International Conference on Statistics in Science, Business and Engineering (ICSSBE) 2012, 10-12 September 2012, Langkawi, Malaysia.

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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 B(u,v) and variance covariance matrix of error 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.

Item Type:	Conference (Paper)
Keywords:	MGWR; estimation; variance-covariance; algorithm; matrix laboratory
Subjects:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics 08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080204 Mathematical Software
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Prof SRI HARINI
Date Deposited:	27 Jun 2016 19:19

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