Roger M. Cooke – författare

This is an extensive survey and critical examination of the literature on the use of expert opinion in scientific inquiry and policy making.Cooke considers how expert opinion is being used today, how an expert's uncertainty is or should be represented, how people do or should reason with uncertainty, how the quality and usefulness of expert opinion can be assessed, and how the views of several experts might be combined. He argues for the importance of developing practical models with a transparent mathematical foundation for the use of expert opinion in science, and presents three tested models. Detailed case studies illustrate how they can be applied to a diversity of real problems in engineering and planning.

This book is an extensive survey and critical examination of the literature on the use of expert opinion in scientific inquiry and policy making. The elicitation, representation, and use of expert opinion is increasingly important for two reasons: advancing technology leads to more and more complex decision problems, and technologists are turning in greater numbers to "expert systems" and other similar artifacts of artificial intelligence. Cooke here considers how expert opinion is being used today, how an expert''s uncertainty is or should be represented, how people do or should reason with uncertainty, how the quality and usefulness of expert opinion can be assessed, and how the views of several experts might be combined. He argues for the importance of developing practical models with a transparent mathematic foundation for the use of expert opinion in science, and presents three tested models, termed "classical," "Bayesian," and "psychological scaling." Detailed case studies illustrate how they can be applied to a diversity of real problems in engineering and planning.

A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances In today's scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. Uncertainty Modeling in Dose Response is the first book of its kind to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. This volume gathers leading researchers in the field to properly address the issue while communicating concepts from diverse viewpoints and incorporating valuable insights. The result is a collection that reveals the properties, strengths, and weaknesses that exist in the various approaches to bench test problems.This book works with four bench test problems that were taken from real bioassay data for hazardous substances currently under study by the United States Environmental Protection Agency (EPA). The use of actual data provides readers with information that is relevant and representative of the current work being done in the field. Leading contributors from the toxicology and risk assessment communities have applied their methods to quantify model uncertainty in dose response for each case by employing various approaches, including Benchmark Dose Software methods, probabilistic inversion with isotonic regression, nonparametric Bayesian modeling, and Bayesian model averaging. Each chapter is reviewed and critiqued from three professional points of view: risk analyst/regulator, statistician/mathematician, and toxicologist/epidemiologist. In addition, all methodologies are worked out in detail, allowing readers to replicate these analyses and gain a thorough understanding of the methods.Uncertainty Modeling in Dose Response is an excellent book for courses on risk analysis and biostatistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for risk assessment, toxicology, biostatistics, and environmental chemistry professionals who wish to expand their knowledge and expertise in statistical dose response modeling problems and approaches.

A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances

In today''s scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. Uncertainty Modeling in Dose Response is the first book of its kind to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. This volume gathers leading researchers in the field to properly address the issue while communicating concepts from diverse viewpoints and incorporating valuable insights. The result is a collection that reveals the properties, strengths, and weaknesses that exist in the various approaches to bench test problems.

This book works with four bench test problems that were taken from real bioassay data for hazardous substances currently under study by the United States Environmental Protection Agency (EPA). The use of actual data provides readers with information that is relevant and representative of the current work being done in the field. Leading contributors from the toxicology and risk assessment communities have applied their methods to quantify model uncertainty in dose response for each case by employing various approaches, including Benchmark Dose Software methods, probabilistic inversion with isotonic regression, nonparametric Bayesian modeling, and Bayesian model averaging. Each chapter is reviewed and critiqued from three professional points of view: risk analyst/regulator, statistician/mathematician, and toxicologist/epidemiologist. In addition, all methodologies are worked out in detail, allowing readers to replicate these analyses and gain a thorough understanding of the methods.

Uncertainty Modeling in Dose Response is an excellent book for courses on risk analysis and biostatistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for risk assessment, toxicology, biostatistics, and environmental chemistry professionals who wish to expand their knowledge and expertise in statistical dose response modeling problems and approaches.

Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis.All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion.Numerous worked examples and applications.Workbook problems, enabling use for teaching.Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors.A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples.Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples.

Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

This title is written for the numerate nonspecialist, and hopes to serve three purposes. First it gathers mathematical material from diverse but related fields of order statistics, records, extreme value theory, majorization, regular variation and subexponentiality. All of these are relevant for understanding fat tails, but they are not, to our knowledge, brought together in a single source for the target readership. Proofs that give insight are included, but for most fussy calculations the reader is referred to the excellent sources referenced in the text. Multivariate extremes are not treated. This allows us to present material spread over hundreds of pages in specialist texts in twenty pages. Chapter 5 develops new material on heavy tail diagnostics and gives more mathematical detail. Since variances and covariances may not exist for heavy tailed joint distributions, Chapter 6 reviews dependence concepts for certain classes of heavy tailed joint distributions, with a view to regressing heavy tailed variables.

Second, it presents a new measure of obesity. The most popular definitions in terms of regular variation and subexponentiality invoke putative properties that hold at infinity, and this complicates any empirical estimate. Each definition captures some but not all of the intuitions associated with tail heaviness. Chapter 5 studies two candidate indices of tail heaviness based on the tendency of the mean excess plot to collapse as data are aggregated. The probability that the largest value is more than twice the second largest has intuitive appeal but its estimator has very poor accuracy. The Obesity index is defined for a positive random variable X as:

Ob(X) = P (X1 +X4 > X2 +X3|X1 ≤ X2 ≤ X3 ≤ X4), Xi independent copies of X.

For empirical distributions, obesity is defined by bootstrapping. This index reasonably captures intuitions of tail heaviness. Among its properties, if α > 1 then Ob(X)

Third and most important, we hope to convince the reader that fat tail phenomena pose real problems; they are really out there and they seriously challenge our usual ways of thinking about historical averages, outliers, trends, regression coefficients and confidence bounds among many other things. Data on flood insurance claims, crop loss claims, hospital discharge bills, precipitation and damages and fatalities from natural catastrophes drive this point home. While most fat tailed distributions are ”bad”, research in fat tails is one distribution whose tail will hopefully get fatter.

This title is written for the numerate nonspecialist, and hopes to serve three purposes. First it gathers mathematical material from diverse but related fields of order statistics, records, extreme value theory, majorization, regular variation and subexponentiality. All of these are relevant for understanding fat tails, but they are not, to our knowledge, brought together in a single source for the target readership. Proofs that give insight are included, but for most fussy calculations the reader is referred to the excellent sources referenced in the text. Multivariate extremes are not treated. This allows us to present material spread over hundreds of pages in specialist texts in twenty pages. Chapter 5 develops new material on heavy tail diagnostics and gives more mathematical detail. Since variances and covariances may not exist for heavy tailed joint distributions, Chapter 6 reviews dependence concepts for certain classes of heavy tailed joint distributions, with a view to regressing heavy tailed variables.

Second, it presents a new measure of obesity. The most popular definitions in terms of regular variation and subexponentiality invoke putative properties that hold at infinity, and this complicates any empirical estimate. Each definition captures some but not all of the intuitions associated with tail heaviness. Chapter 5 studies two candidate indices of tail heaviness based on the tendency of the mean excess plot to collapse as data are aggregated. The probability that the largest value is more than twice the second largest has intuitive appeal but its estimator has very poor accuracy. The Obesity index is defined for a positive random variable X as:

Ob(X) = P (X1 +X4 > X2 +X3|X1 ≤ X2 ≤ X3 ≤ X4), Xi independent copies of X.

For empirical distributions, obesity is defined by bootstrapping. This index reasonably captures intuitions of tail heaviness. Among its properties, if α > 1 then Ob(X)

Third and most important, we hope to convince the reader that fat tail phenomena pose real problems; they are really out there and they seriously challenge our usual ways of thinking about historical averages, outliers, trends, regression coefficients and confidence bounds among many other things. Data on flood insurance claims, crop loss claims, hospital discharge bills, precipitation and damages and fatalities from natural catastrophes drive this point home. While most fat tailed distributions are ”bad”, research in fat tails is one distribution whose tail will hopefully get fatter.