Recent Studies on Risk Analysis and Statistical Modeling [electronic resource] /
Oliveira, Teresa A.editor.
Kitsos, Christos P.editor.
Oliveira, Amílcar.editor.
Grilo, Luís.editor.
This book provides an overview of the latest developments in the field of risk analysis (RA). Statistical methodologies have long-since been employed as crucial decision support tools in RA. Thus, in the context of this new century, characterized by a variety of daily risks - from security to health risks - the importance of exploring theoretical and applied issues connecting RA and statistical modeling (SM) is self-evident. In addition to discussing the latest methodological advances in these areas, the book explores applications in a broad range of settings, such as medicine, biology, insurance, pharmacology and agriculture, while also fostering applications in newly emerging areas. This book is intended for graduate students as well as quantitative researchers in the area of RA.
Includes bibliographical references and index.
Preface -- Introduction -- Part I. Risk Methodologies and Applications -- Assessment of maximum a posteriori image estimation algorithms for reduced acquisition time medical positron emission tomography data -- On mixed cancer risk assessment -- Traditional versus alternative risk measures in hedge fund investment efficiency -- Estimating the extremal coefficient: a simulation comparison of methods -- On a business condence index and its data analytics: A Chilean case -- On the Application of Sample Coefficient of Variation for Managing Loan Portfolio Risks -- Acceptance-sampling plans for reducing the risk associated with chemical compounds -- Risk of Return Levels for Spatial Extreme Events -- Nonparametric Individual Control Charts for Silica in Water -- Revisiting resampling methods in the extremal index estimation: improving risk assessment -- Improving Asymptotically Unbiased Extreme Value Index Estimation -- Hazard Rate and Future Lifetime for the Generalized Normal Distribution -- Part II. Statistical Modeling and Risk issues in several areas -- Wavelet-based detection of outliers in Poisson INAR(1) time series -- Surveillance in Discrete Time Series -- On the maxima of integer models based on a new thinning operator -- Exact and Approximate Probabilities for the Null Distribution of Bartels Randomness Test -- Gamma-series representations for the sum of independent gamma random variables and for the product of independent beta random variables -- Likelihood ratio tests for equality of mean vectors with circular covariance matrices -- Optimal Estimators in Mixed Models with Orthogonal Block Struc- tures -- Constructing Random Klein surfaces without boundary -- Performance Analysis of a GPS Equipment -- Multivariate generalized Birnbaum-Saunders models applied to case studies in bio-engineering and industry -- Energy Prices Forecasting Using GLM -- Pseudo Maximum Likelihood and Moments Estimators for some Ergodic Diffusions -- Statistical Modelling of Counts with a Simple Integer-Valued Bi-linear Process -- A comparative study of the estimators for the demand of Engi- neering Courses in Portugal -- Statistical Methods for Word Association in Text Mining.
This book provides an overview of the latest developments in the field of risk analysis (RA). Statistical methodologies have long-since been employed as crucial decision support tools in RA. Thus, in the context of this new century, characterized by a variety of daily risks - from security to health risks - the importance of exploring theoretical and applied issues connecting RA and statistical modeling (SM) is self-evident. In addition to discussing the latest methodological advances in these areas, the book explores applications in a broad range of settings, such as medicine, biology, insurance, pharmacology and agriculture, while also fostering applications in newly emerging areas. This book is intended for graduate students as well as quantitative researchers in the area of RA.
Statistics .
Quality control.
Reliability.
Industrial safety.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Quality Control, Reliability, Safety and Risk.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.