Outlier detection and accommodation in meta-regression models

Min Zhang, Yong Li, Jun Lu and Lei Shi

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 8, 1728-1744

Abstract: This article studies outlier detection and accommodation under mean-shift model in random effect meta-regression model. We present two approaches for multiple outliers detection in terms of well known mean-shift model. One use traditional way as in classical regression model to derive the test statistic in which the heterogeneity variance parameter is fixed first and then replaced by its estimator under the null model; this method is called as one-step approximation. However, this one-step approximation ignores the impact of heterogeneity variance estimation if the outliers detected are highly influential on estimating heterogeneity variance. Therefore, we propose a new approximate method to derive test statistic of multiple outliers in meta-regression model, in which the effects of estimating heterogeneity variance are taken into consideration. The formulae based on MLE and REMLE are both considered. The power performance of test statistics are examined and compared via simulation studies and we find our approximate method is more effective to identifying outliers in meta-regression models. In addition, we propose a modified model to accommodate outliers in meta-regression model. Two examples are analyzed for illustration.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1652321 (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:50:y:2021:i:8:p:1728-1744

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

DOI: 10.1080/03610926.2019.1652321

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 ().