skip to main content

LIFE EXPECTANCY MODELING USING MODIFIED SPATIAL AUTOREGRESSIVE MODEL

*Hasbi Yasin orcid scopus publons  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Budi Warsito  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Arief Rachman Hakim  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Rahmasari Nur Azizah  -  Data Science Institute, I-Biostat, Hasselt University Belgium, Belgium
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.

Note: This article has supplementary file(s).

Fulltext View|Download |  common.other
Turnitin Check
Subject
Type Other
  Download (2MB)    Indexing metadata
Keywords: Life Expectancy; MSOM; Outlier detection; SAR
Funding: DRPM BRIN-PDUPT under contract 225-92/UN7.6.1/PP/2021

Article Metrics:

  1. (BPS-Statistics of Jawa Tengah Province). (2018). Profil Kesehatan Provinsi Jawa Tengah 2017. BPS-Statistics of Jawa Tengah Province. https://jateng.bps.go.id/publication/ 2018/08/03/0392a381b71c2bc8f708f794/profil-kesehatan-provinsi-jawa-tengah-2017.html
  2. (BPS-Statistics of Jawa Tengah Province). (2019). Jawa Tengah Province in Figures 2019. BPS-Statistics of Jawa Tengah Province
  3. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers
  4. Anselin, L. (1992). Spatial Data Analysis with GIS: An Introduction to Application in the Social Sciences, Technical Report 92-10. August
  5. Beckman, R. J., & Cook, R. D. (1983). Outlier... ... .... s. Technometrics, 25(2), 119–149. https://doi.org/10.1080/00401706.1983.10487840
  6. Bivand, R. et al. (2022). spatialreg: Spatial Regression Analysis Version 1.2-3
  7. Dai, X., Jin, L., Shi, A., & Shi, L. (2016). Outlier Detection and Accommodation in General Spatial Models. Statistical Methods and Applications, 25(3), 453–475. https://doi.org/10.1007/s10260-015-0348-1
  8. Draper, N. R., & Smith, H. (1998). Applied Regression Analysis Third Edition (3rd ed.). John Wiley & Sons, Inc
  9. Genton, M. G. (1998). Spatial Breakdown Point of Variogram Estimators. Math Geol, 30, 853–871
  10. Genton, M. G. (2001). Robustness Problems in the Analysis of Spatial Data, Spatial Statistics: Methodological Aspects and Applications (M. Moore (ed.); 159th ed.). Springer Lecture Notes in Statistics
  11. Hakim, A. R., Warsito, B., & Yasin, H. (2020). Live Expectancy Modelling using Spatial Durbin Robust Model. Journal of Physics: Conference Series, 1655(1). https://doi.org/10.1088/1742-6596/1655/1/012098
  12. Hakim, A. R., Yasin, H., & Rusgiyono, A. (2019). Modeling Life Expectancy in Central Java Using Spatial Durbin Model. Media Statistika, 12(2): 152-163. https://doi.org/10.14710/medstat.12.2.152–163
  13. LeSage, J. P. (1999). The Theory and Practice of Spatial Econometrics. University of Toledo
  14. LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. Taylor & Francis Group
  15. Militin, A. F., Palacios, M. B., & Ugarte, M. D. (2003). Robust Trend Parameters in a Multivariate Spatial Linear Model. Test, 12(2), 445–457
  16. Mukrom, M. H., Yasin, H., & Hakim, A. R. (2021). Pemodelan Angka Harapan Hidup Provinsi Jawa Tengah Menggunakan Robust Spatial Durbin Model. Jurnal Gaussian, 10(1), 44–54. https://doi.org/10.14710/j.gauss.v10i1.30935
  17. Musyarofah, H., Yasin, H., & Tarno, T. (2020). Robust Spatial Autoregressive untuk Pemodelan Angka Harapan Hidup Provinsi Jawa Timur. Jurnal Gaussian, 9(1), 26–40. https://doi.org/https://doi.org/10.14710/j.gauss.v9i1.27521
  18. R Team. (2021). shiny: Web Application Framework for R
  19. Shi, L., & Chen, G. (2009). Influence Measures for General Linear Models with Correlated Errors. Am Stat, 63(1), 40–42
  20. Yasin, H., Warsito, B., & Hakim, A. R. (2020). Development Life Expectancy Model in Central Java Using Robust Spatial Regression With M-Estimators. Communications in Mathematical Biology and Neuroscience, 2020(69), 1–16. https://doi.org/10.28919/cmbn/4984

Last update:

No citation recorded.

Last update: 2024-03-28 19:33:47

No citation recorded.