Extreme value analysis in biometrics

Hüsler, Jürg

doi:10.1002/bimj.200800239

Cited by 5 publications

(2 citation statements)

References 61 publications

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“…This approach helps to balance the sample size needed to assure statistical validity with the goal of identifying “real” extreme values. In practice, the top 5–10% of observations are selected (Hüsler, 2009). …”

Section: Introductionmentioning

confidence: 99%

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Jia

Batterman

2012

Atmospheric Environment

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Extreme value theory, which characterizes the behavior of tails of distributions, is potentially well-suited to model exposures and risks of pollutants. In this application, it emphasizes the highest exposures, particularly those that may be high enough to present acute or chronic health risks. The present study examines extreme value distributions of exposures and risks to volatile organic compounds (VOCs). Exposures of 15 different VOCs were measured in the Relationship between Indoor, Outdoor and Personal Air (RIOPA) study, and ten of the same VOCs were measured in the nationally representative National Health and Nutrition Examination Survey (NHANES). Both studies used similar sampling methods and study periods. Using the highest 5 and 10% of measurements, generalized extreme value (GEV), Gumbel and lognormal distributions were fit to each VOC in these two large studies. Health risks were estimated for individual VOCs and three VOC mixtures. Simulated data that matched the three types of distributions were generated and compared to observations to evaluate goodness-of-fit. The tail behavior of exposures, which clearly neither fit normal nor lognormal distributions for most VOCs in RIOPA, was usually best fit by the 3-parameter GEV distribution, and often by the 2-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extrema. Among the RIOPA VOCs, 1,4-dichlorobenzene (1,4-DCB) caused the greatest risks, e.g., for the top 10% extrema, all individuals had risk levels above 10−4, and 13% of them exceeded 10−2. NHANES had considerably higher concentrations of all VOCs with two exceptions, methyl tertiary-butyl ether and 1,4-DCB. Differences between these studies can be explained by sampling design, staging, sample demographics, smoking and occupation. This analysis shows that extreme value distributions can represent peak exposures of VOCs, which clearly are neither normally nor lognormally distributed. These exposures have the greatest health significance, and require accurate modeling.

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Section: Introductionmentioning

confidence: 99%

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Jia

Batterman

2012

Atmospheric Environment

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“…The conference's closing session featured a lecture by Jürgen Hüsler on extreme value statistics including applications to medicine, sports sciences and biology. An account of his lecture is published in this issue (Hüsler, 2009).…”

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confidence: 99%

Statistics and Life Sciences 2008 – First Conference of the Central European Network of the International Biometric Society

Friede

Held²

2009

Biometrical J

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The distribution of blow fly (Diptera: Calliphoridae) larval lengths and its implications for estimating post mortem intervals

2015

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The length or stage of development of blow fly (Diptera: Calliphoridae) larvae may be used to estimate a minimum postmortem interval, often by targeting the largest individuals of a species in the belief that they will be the oldest. However, natural variation in rate of development, and therefore length, implies that the size of the largest larva, as well as the number of larvae longer than any stated length, will be greater for larger cohorts. Length data from the blow flies Protophormia terraenovae and Lucilia sericata were collected from one field-based and two laboratory-based experiments. The field cohorts contained considerably more individuals than have been used for reference data collection in the literature. Cohorts were shown to have an approximately normal distribution. Summary statistics were derived from the collected data allowing the quantification of errors in development time which arise when different sized cohorts are compared through their largest larvae. These errors may be considerable and can lead to overestimation of postmortem intervals when making comparisons with reference data collected from smaller cohorts. This source of error has hitherto been overlooked in forensic entomology.

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Extreme value analysis in biometrics

Cited by 5 publications

References 61 publications

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Statistics and Life Sciences 2008 – First Conference of the Central European Network of the International Biometric Society

The distribution of blow fly (Diptera: Calliphoridae) larval lengths and its implications for estimating post mortem intervals

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