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Overall, the book gives a clear and systematic presentation of models and methods. It will be an excellent source for theoretical and applied statisticians who are interested in research on change-point analysis and its applications to many areas.   —Mathematical Reviews (Review of the First Edition) Revised and expanded, Parametric Statistical Change Point Analysis, Second Edition is an in-depth study of the change point problem from a general point of view, and a deeper look at change point analysis of the most commonly used statistical models. For some time, change point problems have appeared throughout the sciences in such disciplines as economics, medicine, psychology, signal processing, and geology; more recently, they have also been found extensively in applications related to biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data. These areas of interest—new and old—have motivated substantial research on change point problems and led to a significant body of literature in the field. The present monograph stands as a valuable contribution to this literature. Key features and topics:  * Clear and systematic exposition with a great deal of introductory material included;  * Different models in each chapter, including gamma and exponential models, rarely examined thus far in the literature;  * Extensive examples to emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches;  * An up-to-date comprehensive bibliography and two indices. New to the Second Edition:  * New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control;  * Two new sections of applications of the underlying change point models in analyzing the array Comparative Genomic Hybridization (aCGH) data for DNA copy number changes;  * A new chapter on change points in the hazard function;  * A new chapter on other practical change point models, such as the epidemic change point model and a smooth-and-abrupt change point model. This monograph will be a highly useful resource for an impressively broad range of researchers in statistics, as well as a useful supplement for graduate courses in the field.

Change-point problems. --- Change-point problems --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Change-point detection --- Detection, Change-point --- Problems, Change-point --- Statistics. --- Applied mathematics. --- Engineering mathematics. --- Probabilities. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Mathematical statistics --- Mathematical statistics. --- Distribution (Probability theory. --- Mathematics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Engineering --- Engineering analysis --- Mathematical analysis

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This volume provides a collection of exercises together with their solutions in design and analysis of experiments. The theoretical results, essential for understanding, are given first. These exercises have been collected during the authors teaching courses over a long period of time. These are particularly helpful to the students studying the design of experiments and instructors and researchers engaged in the teaching and research of design by experiment. D. G. Kabe retired as Professor of Statistics from St. Mary's University in Canada, having taught statistics and guided Ph.D. students there. Earlier he has been a faculty member at the Dalhousie University, Northern Michigan University, and Wayne State University. He is the author/co-author of more than two hundred research papers and two books. His research interests include design and analysis of experiments, and multivariate statistical analysis. Arjun K. Gupta is Distinguished University Professor and Professor of Mathematics and Statistics at Bowling Green State University, Bowling Green, Ohio. He has written more than 35 invited conferences, symposia, and journal papers and given more than 100 talks at national and international meetings during his 30-plus-year career. He is the co-author or co-editor of 12 books and has written more than 300 research articles. His main areas of interest include multivariate statistical analysis, distribution theory, and change point analysis. He is a Fellow of the American Statistical Association, the Institute of Statisticians, the Royal Statistical Society of England, and the Ohio Academy of Science, and an elected member of the International Statistical Institute.

Statistics. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Experimental design -- Problems, exercises, etc. --- Experimental design. --- Experimental design --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Experiments --- Methodology --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Elliptically Contoured Models in Statistics and Portfolio Theory fully revises the first detailed introduction to the theory of matrix variate elliptically contoured distributions. There are two additional chapters, and all the original chapters of this classic text have been updated. Resources in this book will be valuable for researchers, practitioners, and graduate students in statistics and related fields of finance and engineering. Those interested in multivariate statistical analysis and its application to portfolio theory will find this text immediately useful. In multivariate statistical analysis, elliptical distributions have recently provided an alternative to the normal model. Elliptical distributions have also increased their popularity in finance because of the ability to model heavy tails usually observed in real data. Most of the work, however, is spread out in journals throughout the world and is not easily accessible to the investigators. A noteworthy function of this book is the collection of the most important results on the theory of matrix variate elliptically contoured distributions that were previously only available in the journal-based literature. The content is organized in a unified manner that can serve an a valuable introduction to the subject.

Statistical science --- Economics --- Mathematical statistics --- Business economics --- economie --- statistiek --- econometrie --- wiskunde --- statistisch onderzoek

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With an emphasis on models and techniques, this textbook introduces many of the fundamental concepts of stochastic modeling that are now a vital component of almost every scientific investigation. These models form the basis of well-known parametric lifetime distributions such as exponential, Weibull, and gamma distributions, as well as change-point and mixture models. The authors also consider more general notions of non-parametric lifetime distribution classes. In particular, emphasis is placed on laying the foundation for solving problems in reliability, insurance, finance, and credit risk. Exercises and solutions to selected problems accompany each chapter in order to allow students to explore these foundations. The key subjects covered include: * Exponential distributions and the Poisson process * Parametric lifetime distributions * Non-parametric lifetime distribution classes * Multivariate exponential extensions * Association and dependence * Renewal theory * Problems in reliability, insurance, finance, and credit risk This work differs from traditional probability textbooks in a number of ways. Since no measure theory knowledge is necessary to understand the material and coverage of the central limit theorem and normal theory related topics has been omitted, the work may be used as a single-semester senior undergraduate or first-year graduate textbook as well as in a second course on probability modeling. Many of the chapters that examine central topics in applied probability can be read independently, allowing both instructors and readers extra flexibility in their use of the book. Probability and Statistical Models is for a wide audience including advanced undergraduate and beginning-level graduate students, researchers, and practitioners in mathematics, statistics, engineering, and economics.

Distribution (Probability theory). --- Mathematical statistics. --- Stochastic processes. --- Distribution (Probability theory) --- Mathematical statistics --- Stochastic processes --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Business mathematics. --- Statistical inference --- Statistics, Mathematical --- Arithmetic, Commercial --- Business --- Business arithmetic --- Business math --- Commercial arithmetic --- Finance --- Statistical methods --- Mathematics. --- Economics, Mathematical. --- Mathematical models. --- Probabilities. --- Statistics. --- Applied mathematics. --- Engineering mathematics. --- Probability Theory and Stochastic Processes. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Appl.Mathematics/Computational Methods of Engineering. --- Mathematical Modeling and Industrial Mathematics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Quantitative Finance. --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution (Probability theory. --- Finance. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Mathematical and Computational Engineering. --- Funding --- Funds --- Economics --- Currency question --- Engineering --- Engineering analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Models, Mathematical --- Simulation methods --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Methodology

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With an emphasis on models and techniques, this textbook introduces many of the fundamental concepts of stochastic modeling that are now a vital component of almost every scientific investigation. These models form the basis of well-known parametric lifetime distributions such as exponential, Weibull, and gamma distributions, as well as change-point and mixture models. The authors also consider more general notions of non-parametric lifetime distribution classes. In particular, emphasis is placed on laying the foundation for solving problems in reliability, insurance, finance, and credit risk. Exercises and solutions to selected problems accompany each chapter in order to allow students to explore these foundations. The key subjects covered include: * Exponential distributions and the Poisson process * Parametric lifetime distributions * Non-parametric lifetime distribution classes * Multivariate exponential extensions * Association and dependence * Renewal theory * Problems in reliability, insurance, finance, and credit risk This work differs from traditional probability textbooks in a number of ways. Since no measure theory knowledge is necessary to understand the material and coverage of the central limit theorem and normal theory related topics has been omitted, the work may be used as a single-semester senior undergraduate or first-year graduate textbook as well as in a second course on probability modeling. Many of the chapters that examine central topics in applied probability can be read independently, allowing both instructors and readers extra flexibility in their use of the book. Probability and Statistical Models is for a wide audience including advanced undergraduate and beginning-level graduate students, researchers, and practitioners in mathematics, statistics, engineering, and economics.

Statistical science --- Finance --- Economics --- Operational research. Game theory --- Mathematical statistics --- Probability theory --- Mathematics --- Applied physical engineering --- Engineering sciences. Technology --- Planning (firm) --- Business economics --- Computer. Automation --- kennis --- ICT (informatie- en communicatietechnieken) --- toegepaste wiskunde --- waarschijnlijkheidstheorie --- stochastische analyse --- economie --- mathematische modellen --- statistiek --- financiën --- econometrie --- wiskunde --- ingenieurswetenschappen --- kansrekening --- statistisch onderzoek