Quantile regression for linear models with autoregressive errors using EM algorithm
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In this paper, we consider the quantile linear regression models with autoregressive errors. By incorporating the expectation–maximization algorithm into the considered model, the iterative weighted least square estimators for quantile regression parameters and autoregressive parameters are derived. Finally, the proposed procedure is illustrated by simulations and a real data example.
KeywordsMaximum likelihood estimation (MLE) Hierarchical model QR analysis Autoregressive model
The authors thank editors and two reviewers for their constructive comments and valuable suggestions which have greatly improved the paper. The work is partly supported by Young academic leaders project of Henan University of Science and Technology (No. 13490008), National Natural Science Foundation of China (No. 11501167) and China Postdoctoral Science Foundation (No. 2017M610156).