JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

Submit Article

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

Authors

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

Keywords:

Sea Surface Temperature (SST), median imputation, S-estimator, LTS-estimator, R-squared $(R^2)$.

DOI:

https://doi.org/10.17654/0973514323012

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 $(R^2)$ 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 $R^2$ 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 $R^2$ value.

Received: February 6, 2023 
Accepted: March 15, 2023

References

F. A. Schott, S. P. Xie and J. P. McCreary, Jr., Indian Ocean circulation and climate variability, Rev. Geophys. 47(RG1002) (2009), 5-7.

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.

E. Mulyana, Hubungan antara Anomali Suhu Permukaan Laut dengan Curah Hujan di Jawa, Jurnal Sains dan Tekhnologi Modifikasi Cuaca 2(8) (2000), 125-132.

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.

D. Chan, Combining statistical, physical, and historical evidence to improve historical sea-surface temperature records, Harvard Data Science Review 3(1) (2021), 1-29.

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.

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.

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.

P. J. Rousseeuw, Least median squares regression, J. Amer. Statist. Assoc. 79 (1984), 871-880.

V. J. Yohai, High breakdown-point and high efficiency robust estimates for regression, Ann. Statist. 15(2) (1987), 642-656.

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.

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.

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.

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.

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.

Downloads

Published

2023-06-07

Issue

Vol. 23 No. 2 (2023)

Section

Articles

License

Copyright (c) 2023 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

_________________________

Licensing Terms:

Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.

Non-Commercial Use: For non-commercial purposes only. No commercial activities without explicit permission.

No Derivatives: Modifying or creating derivative works not allowed without written permission.

Contact Puspha Publishing House for more info or permissions.

How to Cite

APPLICATION OF ROBUST REGRESSION ON SEA SURFACE TEMPERATURE DATA IN THE INDIAN OCEAN. (2023). JP Journal of Biostatistics, 23(2), 211-225. https://doi.org/10.17654/0973514323012

Similar Articles

1-10 of 33

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)