Abstract
Chaloner and Brant (1988) propose a Bayesian method for identifying outliers in univariate linear models. This paper presents an approach generalizing their idea to multivariate normal samples and multivariate linear models. The posterior distribution of the squared norm of the realized errors is used f o loutlier identification. Bayes factors are used for examining whether or not an observation is an outlier.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
