A Robust Cubic Spline Approach for Hierarchical Regression Models with Outliers

Authors

  • Sarah Sabah Akram College of Administration and Economics, Wasit University, Wasit, Iraq
  • Hayder Talib College of Administration and Economics, Sumer University, Thi-Qar, Iraq
  • Noor Abdul-Kareem Fayadh College of Administration and Economics, Sumer University, Thi-Qar, Iraq

DOI:

https://doi.org/10.19139/soic-2310-5070-3633

Keywords:

hierarchical; Robust Cubic Spline; outlier values

Abstract

This study is based on parameter estimation for hierarchical regression models in the presence of outliers. The robust cubic spline and the maximum likelihood estimation are compared. In order to test the efficiency of the two methods, Monte Carlo simulation experiments were conducted on sample sizes 15, 25, 40, and 60 at three different percentages of outliers 1%, 3%, and 5%. The assessment of performance was done by the absolute deviation error (ADE) and the coefficient of determination (R2). The outcome of this study showed that the robust cubic spline performed better than maximum likelihood estimation.

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Published

2026-05-28

How to Cite

AKRAM, S. S. A., Talib, H., & FAYYADH , N. A.-K. F. (2026). A Robust Cubic Spline Approach for Hierarchical Regression Models with Outliers. Statistics, Optimization & Information Computing, 16(1), 103–115. https://doi.org/10.19139/soic-2310-5070-3633

Issue

Vol. 16 No. 1 (2026)

Section

Research Articles

License

Copyright (c) 2026 Hayder Talib, Sarah Sabah Akram, Noor Abdul-Kareem Fayadh

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