Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

Authors

  • yahya matdoan Program Studi Statistika Universitas Pattimura

DOI:

https://doi.org/10.20956/jmsk.v16i3.8970

Keywords:

Quantile Regression, Outliers, Malaria

Abstract

The OLS method estimation is based on a normal distribution, so it is not appropriate to analyze a number of data that are not symmetrical or contain outliers. Therefore, quantile regression was developed which was not affected by outliers. This study compares quantile regression with OLS in the case of factors affecting malaria in Indonesia. The results show that the value of the Quantil Regression model is 0,832 and the MSE value is 0,182. In addition, the OLS model obtained a value of 0,681 and an MSE value of 0,231. So we get the conclusion that the best model is a quantile regression model. Further results were obtained that the main factors causing the spread of malaria in Indonesia were the factor of livable houses, poor population factors and physician factors.

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Author Biography

yahya matdoan, Program Studi Statistika Universitas Pattimura

Program Studi Statistika Universitas Pattimura

References

Badan Pusat Statistika. (2014). Hasil Survei Sosial Ekonomi Nasional

Tahun 2014. Jakarta.

Budiantara, I.N. (2011), Penelitian Bidang Regresi Spline Menuju

Terwujudnya Penelitian Statistika yang Mandiri dan Berkarakter.

Seminar Nasional FMIPA Universitas Pendidikan Ganesha.

Chen, C. dan Wei. (2005). Computational Issues for Quantile

Regression. The Indian Journal of Statistics. Vol. 67, hal. 399-417.

Daoud, J. I. (2018). Multicollinearity and Regression Analysis. Journal

of Physics: Conference Series, 949(1). https://doi.org/10.1088

/1742-6596/949/1/012009

Ghozali, Imam. 2013. Aplikasi analisis Multivariat dengan program

SPSS. Edisi ketujuh. Semarang.

Gob, S. C. dan Knight, K. (2009). “Nonstandard Quantile-Regression

Inference”. Econometric Theory. Vol. 25, hal. 1415-1432 .

Gujarati, D. N. (2004). Basic Econometrics, 4th Edition. New York:

McGrahill. Co.

Hardle, W. (1990). Applied Nonparametric Regression. Cambridge

University Press. New York.

Hastie, T. J. dan Tibshirani, R. J. (1990). Generalized Additive Models.

Chapman and Hall. New York. London.

Kementrian Kesehatan Republik Indonesia. (2014). Profil kesehatan

Indonesia, Pusat data dan Informasi Kementrian Kesehatan. Jakarta.

Koenker, R. dan Machado, J. A. F. (1999). Goodness of fit and Related

Inference Process for Quantile Regression. Journal of the American

statistical Association. Vol. 94, no 448, hal 1296-1310.

Lin W, Zongwu, Li. (2015). Optimal smoothing in nonparametric

conditional quantile derivative function estimation. Journal of

Econometrics ScienceDirect vol. 188, hal. 502-513.

Mirontoneng, A.R, Ismanto, A.Y dan Malara, R. (2014). Analisis faktor-

faktor yang berhubungan kejadian malaria pada anak di wilayah kerja

PKM Tona kecamatan tahuna kabupaten Sangihe. Program Studi Ilmu

Keperawatan Fakultas kedokeran Universitas Sam Ratulangi Manado.

Schober, P., & Schwarte, L. A. (2018). Correlation coefficients:

Appropriate use and interpretation. Anesthesia and Analgesia, 126(5),

–1768. https://doi.org/0.1213/ANE.0000000000002864.

Susilowati, P, A. (2013). Analisis regresi pada prevalensi malaria

Provinsi Maluku Utara, Malauku, Papua Barat dan Papua dengan faktor

yang mempengaruhinya. Skripsi, Jurusan Statistika Institut Teknologi

Sepuluh Nopember.

Umeh, E. U., & Ojukwu, C. I. (2019). Effects of Influential Outliers in

Local Polynomial Techniques (Smoothing Techniques).8(1),19–22.

https://doi.org/10.5923/j.ijps.20190801.03.

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Published

2020-04-28

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Section

Research Articles