Parameter estimation of multivariate geographically weighted regression model using matrix laboratory

Sri Harini*, Purhadi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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 β∼ h(ui, v i) 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.

Original languageEnglish
Title of host publicationICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering
Subtitle of host publication"Empowering Decision Making with Statistical Sciences"
Pages534-537
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 - Langkawi, Kedah, Malaysia
Duration: 10 Sept 201212 Sept 2012

Publication series

NameICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"

Conference

Conference2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012
Country/TerritoryMalaysia
CityLangkawi, Kedah
Period10/09/1212/09/12

Keywords

  • MGWR
  • algorithm
  • estimation
  • matrix laboratory
  • variance-covariance

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