The asymptotic distribution of robust maximum likelihood estimator with Huber function for the mixed spatial autoregressive model with outliers

Zhen Yang, Yihui Luan and Jiming Jiang

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 18, 6311-6340

Abstract: There is a wide range of outliers in spatial data, and these potential outliers will have a great impact on parameter estimation and corresponding statistical inference. Relying on the framework of maximum likelihood estimation (MLE), we investigate the asymptotic distribution of robust ML estimator under the mixed spatial autoregressive models with outliers and compare it with that of the ML estimator. Furthermore, based on the asymptotic theoretical result, we conduct the confidence interval of robust MLE and MLE. Similar to the results of MLE, we construct the second-order-corrected robust confidence interval using the parametric and semi-parametric bootstrap method. Simulation studies using Monte Carlo show that the robust estimator with the Huber loss function is more accurate and outperforms the MLE in most sample settings when data is contaminated by outliers. Then the use of the method is demonstrated in the analysis of the Neighborhood Crimes Data and the Boston Housing Price Data. The results further support the eligibility of the robust method in practical situations.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2022.2027985 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:52:y:2023:i:18:p:6311-6340

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2022.2027985

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().