Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control
Purpose: Linear autoregressive models are among the most widely used models in time series analysis. However, the presence of outliers and data with heavy-tailed distributions significantly reduces the accuracy of parameter estimation in these models. Therefore, the use of quantile models as an effective solution to address this challenge and improve analyses in fields such as statistical quality control is essential.
Methodology: In this article, the parameters of the Weighted Composite Quantile Autoregressive (WCQAR) model are estimated using a frequentist approach, and its application in statistical quality control is presented. The performance of the proposed method is evaluated through a comprehensive simulation study and the analysis of a real dataset (the percentage weight of a material in the output of a distillation column).
Findings: The results of the simulation and real data analysis show that the SEM algorithm, compared to conventional methods, provides more accurate and stable estimates and effectively overcomes problems caused by outliers and heavy-tailed distributions.
Originality/Value: The main innovation of this research is the use of the SEM algorithm instead of conventional algorithms such as EM to compute the maximum likelihood estimators for the parameters of the weighted mixture quantile autoregressive model. This approach can serve as a powerful and reliable tool in time series analysis and statistical quality control.
Bahmani, M. & Kalantari, M. (2026). Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control. (e255549). Journal of Quality Engineering and Management, (), e255549 https://doi.org/10.48313/jqem.2026.575020.1600
MLA
Bahmani, M., & Kalantari, M. "Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control" .e255549 , Journal of Quality Engineering and Management, , 2026, e255549. doi: 10.48313/jqem.2026.575020.1600
HARVARD
Bahmani, M., & Kalantari, M. (2026). 'Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control', Journal of Quality Engineering and Management, (), e255549. doi: 10.48313/jqem.2026.575020.1600
CHICAGO
Bahmani, M. & Kalantari, M., "Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control." Journal of Quality Engineering and Management, (2026): e255549, doi: 10.48313/jqem.2026.575020.1600
VANCOUVER
Bahmani M., Kalantari M. Parameter Estimation of the Weighted Composite Quantile Autoregressive Model Using the SEM Algorithm and Its Application in Statistical Quality Control. J. Qual. Eng. Manag. 2026;():e255549 (In Persian). doi: 10.48313/jqem.2026.575020.1600
