Diagnostics of Single and Multiple Outliers on Likelihood Distance
Journal Title: American journal of Engineering Research - Year 2018, Vol 7, Issue 3
Abstract
A problem often resist in statistical analysis is that there are may exist some extremely small or large observations. Diagnostics of these observations is a crucial tool of statistical analysis. In this paper, we proposed likelihood distance to detect outliers data points for repeated measurement data. The results indicate us single and multiples outliers data cases. The method has been used to explore the presentation of outliers in nonlinear regression models.
Authors and Affiliations
Munsir Ali, Zamir Ali, Zeinab Ebrahimpour
Keywords
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