@inproceedings{6e6b3fee9a3d4b3f9e7831efa2c4e655,
title = "Parameter estimation of multivariate geographically weighted regression model using matrix laboratory",
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.",
keywords = "MGWR, algorithm, estimation, matrix laboratory, variance-covariance",
author = "Sri Harini and Purhadi",
year = "2012",
doi = "10.1109/ICSSBE.2012.6396622",
language = "English",
isbn = "9781467315821",
series = "ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: {"}Empowering Decision Making with Statistical Sciences{"}",
pages = "534--537",
booktitle = "ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering",
note = "2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 ; Conference date: 10-09-2012 Through 12-09-2012",
}