Pushpa Publishing House

Journal Menu

Content

Volume 24 (2024)

Volume 23 (2023)

	Volume 23, Issue 3 Pg 227 - 327 (November 2023)
	Volume 23, Issue 2 Pg 95 - 225 (July 2023)
	Volume 23, Issue 1 Pg 1 - 94 (March 2023)

Volume 18 (2021)

Volume 17 (2020)

Volume 16 (2019)

Volume 15 (2018)

Volume 14 (2017)

Volume 13 (2016)

Volume 12 (2015)

Volume 11 (2014)

Volume 10 (2013)

Volume 9 (2013)

Volume 8 (2012)

Volume 7 (2012)

Volume 6 (2011)

Volume 5 (2011)

Volume 4 (2010)

Volume 3 (2009)

Volume 2 (2008)

Volume 1 (2007)

JP Journal of Biostatistics

JP Journal of Biostatistics
Volume 23, Issue 2, Pages 211 - 225 (July 2023)
http://dx.doi.org/10.17654/0973514323012

APPLICATION OF ROBUST REGRESSION ON SEA SURFACE TEMPERATURE DATA IN THE INDIAN OCEAN

Norizan Mohamed, Nur Ain Natasha Baharin, Nur Sabrina Mohamad Ikram, Nor Azlida Aleng, Maharani A. Bakar, Siti Madhihah Abdul Malik, Nur Fadhilah Ibrahim and Miftahuddin

Abstract:

Sea Surface Temperature (SST) is the temperature of the water near an ocean’s surface. It plays a critical role in the interaction of the Earth’s surface and atmosphere. However, not all time series data on SST are complete to affect climate change prediction. To address such issues, the median imputation is used to deal with the missing data. In data analysis, outlier is unavoidable, and robust statistical methods are required. The presence of outlier which is common in the dataset leads to an error in the result. To remedy this problem, the robust regression has been proposed. The missing values of SST in the Indian Ocean got imputed by using the median imputation approach. We then construct the robust regression model using the S-estimator and LTS-estimator. The R-squared (R²) values of the S-estimator and LTS-estimator were 0.5670 and 0.6033, respectively. When the data was contaminated with 1%, 2%, 3%, 4% and 5% outliers, the R² values of the LTS-estimator were 0.5899, 0.5811, 0.5740, 0.5767 and 0.5699. The study’s findings revealed that the LTS-estimator is a better model compared to the S-estimator in terms of robustness, as is evidenced by the highest R²-value.

Keywords and phrases:

Sea Surface Temperature (SST), median imputation, S-estimator, LTS-estimator, R-squared R².

Received: February 6, 2023; Accepted: March 15, 2023; Published: June 7, 2023

How to cite this article: Norizan Mohamed, Nur Ain Natasha Baharin, Nur Sabrina Mohamad Ikram, Nor Azlida Aleng, Maharani A. Bakar, Siti Madhihah Abdul Malik, Nur Fadhilah Ibrahim and Miftahuddin, Application of robust regression on sea surface temperature data in the Indian Ocean, JP Journal of Biostatistics 23(2) (2023), 211-225. http://dx.doi.org/10.17654/0973514323012

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:

[1] F. A. Schott, S. P. Xie and J. P. McCreary, Jr., Indian Ocean circulation and climate variability, Rev. Geophys. 47(RG1002) (2009), 5-7.
[2] N. H. Saji, B. N. Goswami, P. N. Vinayachandran and T. Yamagata, Adipole mode in the tropical Indian Ocean, Journal Nature 401 (1999), 360-363.
[3] E. Mulyana, Hubungan antara Anomali Suhu Permukaan Laut dengan Curah Hujan di Jawa, Jurnal Sains dan Tekhnologi Modifikasi Cuaca 2(8) (2000), 125-132.
[4] IPCC, Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007.
[5] D. Chan, Combining statistical, physical, and historical evidence to improve historical sea-surface temperature records, Harvard Data Science Review 3(1) (2021), 1-29.
[6] C. Reid, J. Marshall, D. Logan and D. Kleine, Terumbu Karang dan Perubahan Iklim, Terjemahan dari Coral Reefs and Climate Change, oleh Badan Penelitian dan Pengembangaan Kelautan dan Perikanan and Kementerian Kelautan dan Perikanan Indonesia, Panca Jaya, Jakarta, 2009.
[7] Miftahuddin, O. Fanny, S. Ichsan, M. A. Bakar and N. Mohamed, Detection of missing sea surface temperatures (SST) data in the Indian Ocean, JP Journal of Biostatistics 18(1) (2021), 51-66.
[8] Miftahuddin, N. Mohamed, M. A. Bakar, Nur Shima, Fadhli and S. Ichsan, Prediction of rainfall using ARIMA mixed models, Applied Mathematics and Computational Intelligence 10 (2021), 101-126.
[9] P. J. Rousseeuw, Least median squares regression, J. Amer. Statist. Assoc. 79 (1984), 871-880.
[10] V. J. Yohai, High breakdown-point and high efficiency robust estimates for regression, Ann. Statist. 15(2) (1987), 642-656.
[11] N. Gokmen and A. Erar, Robust estimators in linear regression: a simulation study and an application on health care, 11th International Statistics Days Conference, Bodrum Akyarlar, 2018.
[12] N. A. Aleng, N. N. Naing, N. Mohamed and M. A. Bakar, An alternative algorithm weighted MM-estimation for detection of outliers, JP Journal of Biostatistics 17(2) (2020), 559-568.
[13] D. M. Khan, A. Yaqoob, S. Zubair, M. A. Khan, Z. Ahmad and O. A. Alamri, Applications of robust regression techniques: an econometric approach, Math Probl. Eng. 1(1) (2021), 1-9.
[14] Y. Susanti and H. Pratiwi, M estimation, S estimation, and MM estimation in robust regression, Int. J. Pure Appl. Math. 91(3) (2014), 349-360.
[15] M. Momeni, M. D. Nayeri, A. F. Ghayoumi and H. Ghorbani, Robust regression and its application in financial data analysis, World Academy of Science, Engineering and Technology 47 (2010), 521-526.