Results 11 to 20 of about 2,204,741 (325)
Annals of Global Health, 2019 Background: Binary outcomes—which have two distinct levels (e.g., disease yes/no)—are commonly collected in global health research. The relative association of an exposure (e.g., a treatment) and such an outcome can be quantified using a ratio measure ...
John A. Gallis, Elizabeth L. Turner
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Safety and Health at Work, 2018 Background: The purpose of the work is to develop a system that allows processing of information for analysis and industrial risk management, to monitor the level of industrial safety and to perform necessary measures aimed at the prevention of accidents,
Sergey S. Kudryavtsev +2 more
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Stability of clinical prediction models developed using statistical or machine learning methods [PDF]
Biometrical journal. Biometrische Zeitschrift, 2022 Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model‐building strategy, including the sample size, number of predictors, and analysis method (e.g.,
R. Riley, G. Collins
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A comparison of statistical methods to predict the residual lifetime risk
European Journal of Epidemiology, 2022 Lifetime risk measures the cumulative risk for developing a disease over one’s lifespan. Modeling the lifetime risk must account for left truncation, the competing risk of death, and inference at a fixed age.
Sarah C. Conner +5 more
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Heavy-Tailed Log-Logistic Distribution: Properties, Risk Measures and Applications
Statistics, Optimization and Information Computing, 2021 Heavy tailed distributions have a big role in studying risk data sets. Statisticians in many cases search and try to find new or relatively new statistical models to fit data sets in different fields. This article introduced a relatively new heavy-tailed
Abd-Elmonem A. M. Teamah +2 more
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Assessment of groundwater quality using statistical methods: a case study
Arabian Journal of Geosciences, 2022 Human activities substantially contribute to the rise of various contaminating ionic levels in the water, which translates in a risk to humans, flora, and fauna.
Monica Chakraborty +3 more
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Methods to control disclosure risk of synthetic data created by National Statistics Agencies
International Journal of Population Data Science, 2023 Objectives With the recent explosion of interest in using synthetic data (SD) for disclosure control many NSAs are releasing, or considering releasing. synthetic versions of their administrative data.
Gillian Raab
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Digital Health, 2023 Objective Systematic review of length of stay (LOS) prediction models to assess the study methods (including prediction variables), study quality, and performance of predictive models (using area under receiver operating curve (AUROC)) for general ...
Swapna Gokhale +7 more
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Theoretical and Empirical Study on Risk Measurement Method Statistics and Portfolio Model [PDF]
Journal of Physics: Conference Series, 2019 Abstract The fundamental purpose of securities investment is to obtain benefits. In order to diversify risks, many investors invest many kinds of securities simultaneously to achieve the maximum returns. Risk measurement methods and portfolio model have become the major issues faced with the financial sector.
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Statistical disclosure control for survey data [PDF]
, 2009 Statistical disclosure control refers to the methodology used in the design of the statistical outputs from a survey for protecting the confidentiality of respondents’ answers. The threat to confidentiality is assumed to come from a hypothetical intruder
Chris Skinner, Skinner, Chris
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