Quantile regression for linear models with autoregressive errors using EM algorithm

Yuzhu Tian (), Manlai Tang (), Yanchao Zang () and Maozai Tian ()
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Yuzhu Tian: Henan University of Science and Technology
Manlai Tang: Hang Seng Management College
Yanchao Zang: Henan University of Science and Technology
Maozai Tian: Renmin University of China

Computational Statistics, 2018, vol. 33, issue 4, No 3, 1605-1625

Abstract: Abstract 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.

Keywords: Maximum likelihood estimation (MLE); Hierarchical model; QR analysis; Autoregressive model (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-018-0811-1

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