Outlier Identification in Censored Environmental Time Series
Abstract Number:
3841
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Kirk Cameron (1)
Institutions:
(1) Macstat Consulting, Ltd., N/A
First Author:
Presenting Author:
Abstract Text:
When estimating trends in contaminated media, it is common to jointly observe apparent outliers and non-detects (i.e., left-censored observations). Identifying outliers usually requires de-trending the time series prior to screening for outlying residuals. The screening in turn requires a reference distribution from which to judge outlying points.
The combination of censored data, nonlinear trends, and outliers raises challenges: 1) how to estimate the trend prior to treating non-detects, and vice-versa? 2) how to compute 'censored residuals' from the trend? 3) how to build a reference distribution given substantial censoring?
We formerly proposed a Monte Carlo mixture model that samples non-detects from a class of bounded distributions on the interval (0, DL), where DL is a left-censoring limit. We illustrate how this mixture model can accurately identify outliers by constructing a broad trend using the mean estimate of repeated draws from the mixture model, and studentizing the trend residuals to both flag and down-weight outliers via an appropriate kernel applied to the studentized distance from the trend.
The benefits of this strategy are explored.
Keywords:
Outliers|Left-Censored Data|Time Series|Environmental|Monte Carlo|Trends
Sponsors:
Section on Statistics and the Environment
Tracks:
Environmental and Ecological Monitoring
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