Assessing the Robustness of AR Models in the Presence of
Non-normality: A Simulation Study
Abstract Number:
2615
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Mohamed Mohamed (1), Evrim Oral (2)
Institutions:
(1) Louisiana State University Health Science Center, N/A, (2) LSUHSC School of Public Health, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
In time series modeling, it is common to assume that innovations follow a normal distribution. However, this assumption does not always hold in real-world scenarios. Environmental datasets, in particular, often contain extreme values that violate normality. Through a comprehensive simulation study, we demonstrate that traditional AR(q) models can produce inaccurate results when innovations deviate from normality, especially when they exhibit skewness. Our findings highlight that outliers can distort estimates, introduce bias, and compromise the generalizability of results.
Keywords:
Autoregressive Models|Robustness|Outliers|Skew distributions| |
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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