Application of robust statistical methods to background tracer data characterized by outliers and left-censored data

Accurate analysis of tracer-breakthrough curves is dependent on the removal of measured background concentrations from the measured tracer recovery data. Background concentrations are commonly converted to a single mean background concentration that is subtracted from tracer recovery data. To obtain...

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Veröffentlicht in:Water research (Oxford) 2011-05, Vol.45 (10), p.3107-3118
1. Verfasser: Field, Malcolm S.
Format: Artikel
Sprache:eng
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Zusammenfassung:Accurate analysis of tracer-breakthrough curves is dependent on the removal of measured background concentrations from the measured tracer recovery data. Background concentrations are commonly converted to a single mean background concentration that is subtracted from tracer recovery data. To obtain an improved estimate for the mean background concentration, a statically-robust procedure addressing left-censored data and possible outliers in background concentration data is presented. A maximum likelihood estimate and other robust methods coupled with outlier removal are applied. Application of statically-robust procedures to background concentrations results not only in better estimates for mean background concentration but also results in more accurate quantitative analyses of tracer-breakthrough curves when the mean background concentration is subtracted. ► This study examines the importance of background tracer concentrations on breakthrough curves. ► Calculations consider outliers removal and outliers accommodation. ► Measured concentrations below detection limits are considered in calculations for the sample mean, median, etc. ► Various substitution methods were used in comparison with a maximum likelihood estimation. ► Results suggest the maximum likelihood estimation method may be best after outliers removal.
ISSN:0043-1354
1879-2448
DOI:10.1016/j.watres.2011.03.018