Special issue on imprecision in statistical data analysis

FUZZY INFORMATION AND STOCHASTICS

In applications there occur different forms of uncertainty. The twomost important types are randomness (stochastic variability) and imprecision(fuzziness). In modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. However, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.Since many years t...

Application of Fuzzy Fault Tree Analysis on Oil and Gas Offshore Pipelines

Fault Tree Analysis (FTA) as a Probabilistic Risk Assessment (PRA) method is used to identify basic causes leading to an undesired event, to represent logical relation of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event. To conduct a quantitative FTA, one needs a fault tree along with failure data of the Basic Events (BEs). Someti...

RANDOM FUZZY SETS: A MATHEMATICAL TOOL TO DEVELOP STATISTICAL FUZZY DATA ANALYSIS

Data obtained in association with many real-life random experiments from different fields cannot be perfectly/exactly quantified.hspace{.1cm}Often the underlying imprecision can be suitably described in terms of fuzzy numbers/\values. For these random experiments, the scale of fuzzy numbers/values enables to capture more variability and subjectivity than that of categorical data, and more accur...

Special issue of statistical analysis and data mining

The significance of reporting to the thousandths place: Figuring out the laboratory limitations

OBJECTIVES A request to report laboratory values to a specific number of decimal places represents a delicate balance between clinical interpretation of a true analytical change versus laboratory understanding of analytical imprecision and significant figures. Prostate specific antigen (PSA) was used as an example to determine if an immunoassay routinely reported to the hundredths decimal place...