KONSTRUKSI UJI KESESUAIAN MODEL GEOGRAPHICALLY WEIGHTED POLYNOMIAL REGRESSION

Authors

  • Nur Chamidah

DOI:

https://doi.org/10.20956/jmsk.v15i2.5711

Keywords:

Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model

Abstract

Abstract

Geographically Weighted Polynomial Regression (GWPolR) is a generalization of   Geographically Weighted Regression (GWR) model. By using the generalization, GWPolR has parameters much more than GWR model. In general, excess of the number of parameter will have a higher appropriate value. However, the model which has less parameter will have the excess for easing in application and its interpretation. Nevertheless, when the model has more the parameters, then the model will be better significantly to be used.  Therefore, the aim of this paper is to construct the conformity between hypothesis test with respect to the GWPolR model.

 

Keywords: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model

 

Abstrak

Geographically Weighted Polynomial Regression (GWPolR) merupakan perumuman dari model Geographically Weighted Regression (GWR). Dengan perumuman tersebut, model GWPolR memiliki jumlah parameter yang lebih banyak daripada model GWR. Umumnya, kelebihan model dengan jumlah parameter lebih banyak adalah memiliki nilai kesesuaian lebih tinggi. Sebaliknya, model dengan jumlah parameter yang sedikit memiliki kelebihan berupa kemudahan dalam aplikasi dan interpretasinya. Namun demikian, jika model dengan jumlah parameter yang lebih banyak ternyata secara signifikan lebih baik maka sudah seharusnya model tersebut dipilih untuk digunakan. Oleh karena itu, tujuan paper ini adalah mengkonstruksi uji hipotesis kesesuaian model GWPolR.

 

Kata Kunci:    Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model

References

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. Fotheringham A.S., Brunsdon C., & Chartlon M., 2002. Geographically Weighted Regression: The Analysis Of Spatially Varying Relationships. John Wiley and Sons, USA.

. Leung, Y., Mei, CL., & Zhang, WX., 2000. Statistical Tests for Spatial Nonstationarity based on The Geographically Weighted Regression Model. Environment and Planning A. 32: 9–32.

. Rencher, A.C., & Schaalje, G.G., 2008. Linear Models in Statistics, 2nd Edition. John Willey & Sons, New York, USA.

. Saifudin, T., Fatmawati, dan Chamidah, N., 2017. Perluasan Geographically Weighted Regression Menggunakan Fungsi Polinomial, Prosiding Seminar Nasional Integrasi Matematika dan Nilai Islami, 1[1], Juli 2017, 15-20.

. Yuan, K.H., & Bentler, P.M., 2010. Two Simple Approximations to the Distribution of Quadratic Forms. British Journal of Mathematical and Statistical Psychology, 63(2): 273–291.

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Published

2018-12-20

Issue

Vol. 15 No. 2 (2019): JMSK Vol. 15, No. 2, January 2019

Section

Research Articles

License

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