TY - GEN

T1 - Parameter estimation of geographically weighted regression (GWR) model using weighted least square and its application

AU - Soemartojo, Saskya Mary

AU - Ghaisani, Rima Dini

AU - Siswantining, Titin

AU - Shahab, Mariam Rahmania

AU - Ariyanto, Moch Muchid

N1 - Publisher Copyright:
© 2018 Author(s).

PY - 2018/9/21

Y1 - 2018/9/21

N2 - Linear regression is a method that can be used to model the relationship between a dependent variable to one or more independent variables. There are some assumptions that must be fulfilled in the linear regression model, such as the error term is normally distributed with mean zero, constant error variance (homoscedasticity), and the error between observations are independent. When analyzing spatial data using a linear regression model, sometimes the homoscedastic assumption cannot be fulfilled because data condition on one location can be different with data condition in other location. Geographically Weighted Regression (GWR) model can be used to overcome the spatial heterogeneity problem. Parameters of GWR model can be estimated using Weighted Least Squares (WLS) method as basic of estimating parameters. As the weight is kernel weighting function. Kernel weighting function used in this paper is Gaussian kernel weighting function. There is an example of the GWR model application by using inpatient claims data of PT. XYZ members to see the relationship between the total inpatient cost to the hospitalization duration and hospital's room type for Typhoid Fever. Based on the map of parameter estimation on GWR model, it can be seen that there is a variation of the total inpatient cost in every subjects location. If only the linear regression model is used to analyze this data, there will be a misleading interpretation so that it is suitable to model the data with GWR model.

AB - Linear regression is a method that can be used to model the relationship between a dependent variable to one or more independent variables. There are some assumptions that must be fulfilled in the linear regression model, such as the error term is normally distributed with mean zero, constant error variance (homoscedasticity), and the error between observations are independent. When analyzing spatial data using a linear regression model, sometimes the homoscedastic assumption cannot be fulfilled because data condition on one location can be different with data condition in other location. Geographically Weighted Regression (GWR) model can be used to overcome the spatial heterogeneity problem. Parameters of GWR model can be estimated using Weighted Least Squares (WLS) method as basic of estimating parameters. As the weight is kernel weighting function. Kernel weighting function used in this paper is Gaussian kernel weighting function. There is an example of the GWR model application by using inpatient claims data of PT. XYZ members to see the relationship between the total inpatient cost to the hospitalization duration and hospital's room type for Typhoid Fever. Based on the map of parameter estimation on GWR model, it can be seen that there is a variation of the total inpatient cost in every subjects location. If only the linear regression model is used to analyze this data, there will be a misleading interpretation so that it is suitable to model the data with GWR model.

UR - http://www.scopus.com/inward/record.url?scp=85054196828&partnerID=8YFLogxK

U2 - 10.1063/1.5054485

DO - 10.1063/1.5054485

M3 - Conference contribution

AN - SCOPUS:85054196828

SN - 9780735417304

T3 - AIP Conference Proceedings

BT - International Conference on Science and Applied Science, ICSAS 2018

A2 - Suparmi, M.A.

A2 - Nugraha, Dewanta Arya

PB - American Institute of Physics Inc.

T2 - International Conference on Science and Applied Science, ICSAS 2018

Y2 - 12 May 2018

ER -