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Journals / TWMS Journal of Applied and Engineering Mathematics / 2026 / Volume: 16 Issue: 9

PROPERTIES AND REAL DATA APPLICATION OF THE SINE ALPHA POWER BURR XII REGRESSION MODEL

Ahmed Salih(Wasit University)0000-0002-6109-224X
Amal Sadeq Hamoodi(Mustansiriya University)0000-0001-7496-7933
Pages
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Publication Date
2026-09-07
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Application to a real lung cancer dataset demonstrates that the SAP Burr XII model outperforms classical AFT models.

📌 3 Key Takeaways

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Objective & Hypothesis:We propose the Sine Alpha Power Burr XII (SAP Burr XII) distribution, obtained by applying the Sine Alpha Power transformation to the Burr XII distribution. and develop an accelerated failure time (AFT) regression model, with parameters estimated via maximum likelihood.
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Key Findings & Data:A simulation study across various sample sizes confirms the consistency of the estimators.
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Conclusion & Impact:Application to a real lung cancer dataset demonstrates that the SAP Burr XII model outperforms classical AFT models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Burr XII distribution · Sine Alpha Power transformation · SAP-Burr XII distribution

Abstract

We propose the Sine Alpha Power Burr XII (SAP Burr XII) distribution, obtained by applying the Sine Alpha Power transformation to the Burr XII distribution. and develop an accelerated failure time (AFT) regression model, with parameters estimated via maximum likelihood. A simulation study across various sample sizes confirms the consistency of the estimators. Additionally, model diagnostics based on Cox–Snell, deviance, and randomized quantile residuals are examined. Application to a real lung cancer dataset demonstrates that the SAP Burr XII model outperforms classical AFT models.

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Cite This Article

Salih, A., Hamoodi, A. S. (2026). PROPERTIES AND REAL DATA APPLICATION OF THE SINE ALPHA POWER BURR XII REGRESSION MODEL. TWMS Journal of Applied and Engineering Mathematics, Volume: 16 Issue: 9. https://ajindex.com/articles/1604193

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References (28)

  1. Abdulah, M., Farhan, A., Alrawi, Z., Abbood, M., & Salih, A. (2026). A Novel Compound G-Family: Statistical Characteristics and Applications to Aircraft Windshield Service Durations and Survival Time Data. Statistics, Optimization & Information Computing, 15(6), 4682–4698.
  2. Abdulhadi, D., Ibrahim, W., Al-Saffar, R., Abd, H., & Salih, A. (2025). Performance of the Generalized Shrinkage Estimator in Zero-Inflated Bell Regression.
  3. Alizadeh, M., Rasekhi, Z., Yousof, H. M., & Hamedani, G. G. (2020). The odd log-logistic Burr XII regression model for survival data. Journal of Statistical Computation and Simulation, 90(8), 1452–1472.
  4. Alizadeh, M., Tahir, M. H., Cordeiro, G. M., Mansoor, M., Zubair, M., & Hamedani, G. G. (2017). The odd log-logistic Burr XII distribution: Properties and applications. Journal of Statistical Theory and Applications, 16(2), 225–243.
  5. Almetwally, E. M., & El-Sherpieny, E. A. (2024). Truncated power Burr XII regression model with application to COVID-19 survival times. Journal of Biostatistics and Epidemiology, 10(2), 112–130.
  6. Alwan, A., Anber, J., Ibrahim, W., & Salih, A. (2026). Using Stein’s loss function on Bayesian Estimation of Kumaraswamy-Weibull Distribution Shape Parameters with Application. Statistics, Optimization & Information Computing, 16(2), 940–962.
  7. Bantan, R. A., Jamal, F., Chesneau, C., & Elgarhy, M. (2021). The power Burr XII regression model with applications to COVID-19 data. Mathematics, 9(12), 1425.
  8. Bhatti, F. A., Hamedani, G. G., Yousof, H. M., & Ali, A. (2023). The Gumbel Burr XII regression model for asymmetric heavy-tailed data. Communications in Statistics – Simulation and Computation, 52(4), 1789–1806.
  9. Burr, I. W. (1942). Cumulative frequency functions. The Annals of Mathematical Statistics, 13(2), 215–232.
  10. Cordeiro, G. M., Yousof, H. M., Ramires, T. G., & Ortega, E. M. M. (2020). The log-gamma generalized Weibull regression model with censored data. Journal of Statistical Theory and Practice, 14(2), 1–22.
  11. Cox, D. R., & Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society: Series B, 30(2), 248–265.
  12. Dunn, P. K., & Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5(3), 236–244.
  13. Elgarhy, M., Almetwally, E. M., & Almutiry, W. (2026). The sine exponentiated Burr XII regression model with applications to medical data. Journal of Applied Statistics, 53(1), in press.
  14. Emiliano, P. C. S., Cordeiro, G. M., & da Silva, R. V. (2022). The Burr XII regression model with varying dispersion for counting data. Brazilian Journal of Probability and Statistics, 36(3), 512–533.
  15. Hamedani, G. G., Yousof, H. M., Altun, E., & Alizadeh, M. (2024). The beta Burr XII regression model for modeling credit risk and financial default data. Financial Innovation, 10(1), 45–67.
  16. Hamoodi, A., & Salih, A. (2026). Novel Quantile Regression Model Using Extended Exponential-Geometric Distribution with Real Data Application. Statistics, Optimization & Information Computing, 16(1), 660–683.
  17. Klein, J. P., & Moeschberger, M. L. (2026). Survival Analysis: Techniques for Censored and Truncated Data (4th ed.). Springer.
  18. Korkmaz, M. C¸., & Genç, A. İ. (2021). The log-odd exponential Weibull regression model for modeling lifetime data with covariates. Hacettepe Journal of Mathematics and Statistics, 50(3), 895–913.
  19. Liu, Y., & Wang, X. (2025). Extending the Sine Alpha Power family to multivariate regression settings for longitudinal survival data. Journal of Multivariate Analysis, 187, 104862.
  20. Mansoor, M., Tahir, M. H., Cordeiro, G. M., & Alzaatreh, A. (2022). A log-linear regression model based on the odd alpha-power Weibull distribution. Journal of Statistical Theory and Practice, 16(2), 1–25.
  21. Ozel, G., & Cakmakyapan, S. (2025). Machine learning-enhanced parametric regression using Burr XII-based neural network survival models. Expert Systems with Applications, 213, 118902.
  22. Rasekhi, Z., Alizadeh, M., Yousof, H. M., & Hamedani, G. G. (2024). Gamma exponentiated exponential Weibull regression model for survival data with covariates. Statistical Methods in Medical Research, 33(1), 88–107.
  23. Salih, A., Husien, W., & Abdulah, M. (2025). High Order Statistics From Lambert-Topp-Leone Distribution: Statistical Properties and Applications. Statistics, Optimization & Information Computing, 14(3), 1584–1597.
  24. Souza, L., Junior, W., de Brito, C., Chesneau, C., & Fernandes, R. (2022). The Sine Alpha Power family of distributions: Theory and applications. Mathematics, 10(8), 1325.
  25. Tadikamalla, P. R. (1980). A look at the Burr and related distributions. International Statistical Review, 48(3), 337–344.
  26. Therneau, T. M. (2020). A Package for Survival Analysis in R. R package version 3.2-7.
  27. Yousof, H. M., Alizadeh, M., & Hamedani, G. G. (2023). The Burr XII regression model for extreme values with applications to environmental data. Environmental and Ecological Statistics, 30(4), 567–589.
  28. Zimmer, W. J., Keats, J. B., & Wang, F. K. (1998). The Burr XII distribution in reliability analysis. Journal of Quality Technology, 30(4), 386–394.