Techniques for evaluating discrepant data

Abstract. Internal conversion coefficients involving atomic electrons (ICC) and electron-positron pairs (IPC) are often required to determine transition multipolarities and total transition rates. A new internal conversion coefficient data base, BrIcc has been developed which integrates a number of tabulations on ICC and IPC, as well as Ω(E0) electronic factors. To decide which theoretical internal conversion coefficient table to use the accurately determined experimental α K , α L , α Total and α K /α L values were compared with the new Dirac-Fock calculations using extreme assumptions on the effect of the atomic vacancy. While the overall difference between experiment and theory is less than 1%, our analysis shows preference towards the so called Frozen Orbital approximation, which takes into account the effect of the atomic vacancy.

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“…Three statistical techniques [11] were employed to analyze the data aiming to identify discrepant data points and deduce …”

Section: Statistical Treatment Of the Datamentioning

confidence: 99%

Internal conversion coefficients - How good are they now?

Kibédi

Burrows

Trzhaskovskaya

et al. 2007

Nd2007

128

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“…The procedures used in Ref. [1] to deduce recommended values and their uncertainties are the ''Limitation of Relative Statistical Weights (LRSW)'' [2], the ''Normalized Residuals (NR)'' [3], the ''Rajeval method (Rajeval)'' [4], the ''Median method (Md)'', with the standard deviation calculated as recommended in Ref. [5], the ''Bootstrap method (Boot)'' [6,7], and the ''Extended Bootstrap method (ExtB)'' [8].…”

Section: Comparing Various Methods: Consistency Conditionmentioning

confidence: 99%

Parametric analysis of discrepant sets of data

Helene

2009

Nuclear Instruments and Methods in Physics Research Section A:

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“…There are several techniques developed specifically for this task; for instance, the evaluators of the Nuclear Data Sheets recommend using the Limitation of relative statistical weight (LRSW) method [10], where steps are taken to prevent a single datapoint to have more than 50 % of the total weight in a weighted average. Other, more refined, techniques are proposed in [2,11], the Normalized Residuals technique (NR) and the Rajeval technique (RT), which try to locate possible outlier results and prevent them from having a great influence in the calculation's outcome. All three techniques are described in detail in [2], and the next subsections will only give a rough outline of each.…”

Section: Robust Averagesmentioning

confidence: 99%

Can robust statistics aid in the analysis of NAA results?

Zahn

Genezini

Secco

et al. 2015

J Radioanal Nucl Chem

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In this work, which is part of a larger effort to develop a software to automate instrumental neutron activation analysis calculations, the elemental concentration in a sample was calculated using either a set containing only the gamma-ray peaks recommended in the literature or a set containing all peaks identified. The results for each element were reduced using five tools: the usual unweighted and weighted means, plus the limitation of statistical weight, Normalized Residuals and Rajeval. The results were compared to the certified value for each element, allowing for discussion on the performance of each statistical tool and on the choices of peaks.

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Techniques for evaluating discrepant data

Cited by 64 publications

References 24 publications

Internal conversion coefficients - How good are they now?

Internal conversion coefficients - How good are they now?

Parametric analysis of discrepant sets of data

Can robust statistics aid in the analysis of NAA results?

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