Composite quantile regression based robust empirical likelihood for partially linear spatial autoregressive models

In this paper, we consider the robust estimation for a class of partially linear spatial autoregressive models. By combining empirical likelihood and composite quantile regression methods, we propose a robust empirical likelihood estimation procedure. Under some regularity conditions, the proposed empirical log-likelihood ratio is proved to be asymptotically chi-squared, and the convergence rate of the estimator for nonparametric component is also derived. Some simulation analyses are conducted for further illustrating the performance of the proposed method, and simulation results show that the proposed method is more robust.

Keywords

partially linear spatial autoregressive model, empirical likelihood, robust estimation, composite quantile regression

2010 Mathematics Subject Classification

Primary 62G05, 62G20. Secondary 62G30.

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Received 17 June 2022

Accepted 13 November 2022

Published 19 July 2024