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Adaptive accelerated failure time modeling with a semiparametric skewed error distribution

Author

Listed:
  • Oh, Sangkon
  • Lee, Hyunjae
  • Kang, Sangwook
  • Seo, Byungtae

Abstract

The accelerated failure time (AFT) model is widely used to analyze relationships between variables in the presence of censored observations. However, this model relies on some assumptions such as the error distribution, which can lead to biased or inefficient estimates if these assumptions are violated. To address this issue, a semiparametric modeling approach is introduced, incorporating a flexible skew-normal scale mixture distribution for the error term. This formulation enhances robustness and reduces the risk of model misspecification, thereby improving the reliability of parameter estimation. Theoretical properties such as identifiability and consistency are established, and a computationally efficient estimation algorithm is developed. Simulation studies and empirical analyses confirm that the proposed method achieves accurate and robust estimation across a wide range of scenarios.

Suggested Citation

  • Oh, Sangkon & Lee, Hyunjae & Kang, Sangwook & Seo, Byungtae, 2026. "Adaptive accelerated failure time modeling with a semiparametric skewed error distribution," Computational Statistics & Data Analysis, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:csdana:v:219:y:2026:i:c:s0167947326000265
    DOI: 10.1016/j.csda.2026.108357
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