Suchergebnisse

"Examines the multidisciplinary applications, problems, and case histories in risk modeling, assessment, and management This book examines risk analysis, focusing on quantifying risk and constructing probability in conjunction with real-world decision-making problems, including institutional, organizational, and political considerations. The author presents basic concepts (hierarchical holographic modeling; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision-tree analysis; multi-objective risk impact analysis method); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies.The fourth edition of Risk Modeling, Assessment, and Management features: Expanded chapters covering systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling complex systems of systems with phantom system models; and hierarchical holographic modeling An expanded appendix including the Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer's Dilemma formulated and solved using a deterministic linear model Updated case studies including a new case study on sequential Pareto-optimal decisions made during emergent complex systems of systems A new companion website with over 200 solved exercises and problems that feature risk analysis theories, methodologies, and applications Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more"--

Intro -- Foreword -- Acknowledgments -- Contents -- Introduction -- Abstract -- 1 Forecast -- The Scientific Method -- Anomalies and Crisis -- Paradigms and Epicycles -- Experiments and Observations -- The Empires of the Times -- Determinism and the Butterfly Effect -- Probability and Many-Body Systems -- Forecasts and Decisions -- How Will the Weather Be Tomorrow? -- Extreme Weather Events -- Climate Changes -- Be Prepared for the Unexpected -- Spread of Diseases and New Virus -- Recurrences and Big Data -- Science, Politics and Forecasts -- References -- 2 Crisis -- Realism and Rigor -- The Queen's Question -- Which Crisis? -- There Will Be Growth in the Spring -- The Disappearance of the Time -- The Three Pillars of Equilibrium -- The Myth of Equilibrium -- Efficiency and Unpredictability -- Mathematics as Ornament -- One Hundred Years of Solitude -- Out of Equilibrium -- The Flight of Bird Flocks and the Market's Panic -- The Wild World of the Financial Markets -- Mathematical Monsters -- Paradigms and Predictions -- Economic Complexity -- The Neoclassical Dictatorship -- The Theft of a Successful Brand -- Economics Is Politics -- Cultural Hegemony -- Economics, Politics, Forecasts -- References -- 3 Research -- The Growth in Inequality -- The Techno-evaluation Era -- Evaluation and Creativity -- The Misunderstanding of Competition -- History Teaches but Does not Have Scholars -- The Time of the Great Navigators -- Physics' Woodstock -- Spaces to Make and Correct Mistakes -- Playing with a Sticky Tape -- Primes Takeaways -- Selecting Pink Diamonds -- The Scientific Forger -- Tip of the Iceberg? -- The Dogma of Excellence -- The 'Harvard Here' Model -- References -- 4 Politics -- The Basic Research at the Roots of Innovation -- Micro-Macro Hard Disks -- Applied Research and Applications of Basic Research.

1. Bertrand Russell : 1872-1970 / Gilbert Ryle -- 2. Propositions and sentences / Alan R. White -- 3. Russell's paradox and some others / William C. Kneale -- 4. A diagnosis of the liar and other semantical vicious-circle paradoxes / Charles S. Chihara -- 5. A refutation of an unjustified attack on the axiom of reducibility / John Myhill -- 6. On constrained denotation / Abraham Robinson -- 7. Is philosophy 'an idleness in mathematics'? / Alice Ambrose -- 8. Post Principia / R.L. Goodstein -- 9. Russell and modal logic / Nicholas Rescher -- 10. Russell and Bradley on relations / Timothy Sprigge -- 11. On Russell's critique of Liebniz's philosophy / Stephan Korner -- 12. On some relations between Leibniz's monadology and transfinite set theory : a complement to Russell's thesis on Leibniz / Joel I. Friedman -- 13. The infinite / Morris Lazerowitz -- 14. Belief as a propositional attitude / Alan R. White -- 15. Truth, belief and modes of description / R.M. Martin -- 16. The concern about truth / Frederick L. Will -- 17. Russell and the form of outer sense / Jay F. Rosenberg -- 18. Russell's theory of perception / D.J. O'Connor -- 19. Russell and Schlick : a remarkable agreement on a monistic solution of the mind-body problem / Herbert Feigl -- 20. Self-acquaintance and the meaning of 'I' / G.N.A. Vesey -- 21. Some aspects of knowledge (I) / George W. Roberts -- 22. Russell's philosophical account of probability / Bas C. van Fraassen -- 23. Foundations / Renford Bambrough -- 24. Russell's ethics / W.I. Matson -- 25. Russell's judgement on Bolshevism / Antony Flew -- 26. Solipsistic politics : Russell's empiricist liberalism / Benjamin R. Barber.

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, multivariate volatility and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Journal of the American Statistical Association-Theory and Methods and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes-Monographs. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction. David S. Matteson is Assistant Professor of Statistical Science at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the ...

part PART I Critical Common-sensism -- chapter 1 Terminology -- chapter 2 Cognition ; Material Quality of Cognition ; Theory of Signs -- chapter 3 Every Cognition is Judicative -- chapter 5 Peirce's Thesis; Further on the Usage of 'Intuition' -- chapter 6 Associational not the Same as Causal Determination -- chapter 7 Why Intuitive Cognitions need not be Assumed -- chapter 8 No Necessity of Assuming a 'First' Cognition -- chapter 9 Peirce's Use of 'Self' -- chapter 10 'Percepts' and 'Perceptual Judgments' -- chapter 11 Generality and Vagueness -- chapter 12 Why Intuitive Cognition is not Possible -- chapter 13 Abduction and Induction -- chapter 14 The Abductive and Inductive Character of Thought -- chapter 15 Peirce and the Scottish School -- chapter 16 Three Categories of the Mind; 'Immediate Perception' -- chapter 17 The 'Indubitable' Propositions -- chapter 19 The Meaning of 'Indubitable' -- chapter 20 The Social and Perceptual Indubitables -- chapter 21 'Theoretical' and 'Practical' Beliefs -- chapter 22 Doubt, Belief and Empirical Science -- chapter 23 The Meaning of 'Truth' and of 'Reality' -- chapter 24 The Principle of Fallibilism -- chapter 25 Peirce and the 'Cartesian Tradition' -- chapter 26 The Meaning of 'Common Sense' and of 'Experience' -- chapter 27 Peirce and Contemporary Logical Empiricism -- part PART II Pragmatism -- chapter 28 Origin of Pragmatism -- chapter 29 The Purpose of Pragmatism -- chapter 30 What is an Interpretant? -- chapter 31 Peirce's Limitation of Pragmatism; on Purely Demonstrative Signs -- chapter 32 General and Special Analyses of Meaning -- chapter 33 The Pragmatic Criterion of Meaning -- chapter 34 The Criterion with Respect to Sentences -- chapter 35 Pragmatism and Positivism -- chapter 36 Peirce's Realism -- chapter 37 On Hypotheses -- chapter 38 The Element of Conventionalism in Peirce -- chapter 39 'Purified Philosophy' -- chapter 40 The Second Kind of Utterance on Pragmatism -- chapter 41 Sentences about Feelings -- chapter 42 Peirce and James -- chapter 43 General Remarks on Parts I and II -- part PART III The Formal and the Empirical -- chapter 44 The Nature of Logic -- chapter 45 Leading Principles -- chapter 46 On Fact and Formalism in Logic -- chapter 47 Icon, Index and Symbol -- chapter 48 The Nature of Mathematics -- chapter 49 The Empirical Interpretation of Probability -- chapter 50 Probability and Induction -- chapter 51 Peirce and Kant -- chapter 52 Conclusion.

With the collapse of Demographic Transition Theory, new theories of population must not just be explanations, but should be falsifiable theories which can compute the number of occurrences of marriages and births. This book reviews computable marriage and birth function using dynamic properties. To do that, the functions are defined in high dimensional space. The reaction-diffusion equation of the number of children in a space is applied to these phenomena, providing solutions to many problems concerning a decline in fertility. The functions are developed as stochastic maps based on the present behaviors of successive behaviors in a geographical space. As we assume that there is an inter-dependence of human behaviors, we use the law of dynamics concerning the function of marriage and birth. The exact mathematical definition of interactions in a space naturally implies a causal relation. For the function concerning the number of children of parents, two geographical-dimensional spaces are required. The decline in fertility in Belgium due to different languages is explained, and the longer fertility period in Brittany is explained by the Laplacian of the diffusion equation. Depending on the degree of symbolic control over behaviors, we need to add the degree of the dimension of the space. For the marriage function, we add age as a biological dimension to the geographical space. In this higher dimensional space, the mapping from neighboring present marriages to neighboring successive marriages is no less than that of the marriage function. These chain reactions caused the baby boom as an exothermal reaction-diffusion. Birth functions require one to add the marriage-age dimension to two geographical and age dimensions so that it is a five dimensional hypersurface. It can, thus, determine birth probabilities of a female who married at a certain age. The phenomenon of modern fertility decline may only be the result of these chain reactions. These processes are solely dependent upon time-space, and not on socioeconomic conditions. This is the very reason why we are able to predict it mathematically. The book provides a new thinking in fertility decline for demographic research. Readers need to be aware that the fertility decline experienced throughout the modern era is a spatial pattern formation (as a reaction-diffusion). The author hopes new mathematical applications in human activities are developed through these new models.

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS). Utilizes the popular and accessible IBM SPSS Statistics software package to teach data analysis for business and finance in a step-by-step approach A comprehensive, in-depth guide--especially relative to the competition Explains the statistical assumptions and rationales underpinning application of the IBM SPSS for Statistics package, instead of simply presenting techniques More than 100 color graphs, screenshots, and figures Includes directed download of the software, IBM SPSS Statistics 24 [current version] Abdulkader Aljandali, Ph. D., is Senior Lecturer at Regent's University London. He currently leads the Business Forecasting and the Quantitative Finance module at Regent's in addition to acting as a Visiting Professor for various universities across the UK, Germany and Morocco. Dr Aljandali is an established member of the Higher Education Academy (HEA) and an active member of the British Accounting and Finance Association (BAFA). "This is an excellent book for learning SPSS and a long awaited addition for teaching statistics in business and finance studies. The emphasis is on the effective use of SPSS and on correctly applying and interpreting results. A wonderful guide I have found so far."--Dr Yacine Belghitar - Cranfield University "Dr Aljandali's book fills an important gap in the area of applied economics by making econometric concepts easier to grasp and apply using SPSS software, which makes it an invaluable handbook for students, researchers and practitioners."--Dr Mete Feridun - University of Cambridge.--

This book provides a clear introduction to this important area of statistics. The author provides a wide of coverage of different kinds of multilevel models, and how to interpret different statistical methodologies and algorithms applied to such models. This 4th edition reflects the growth and interest in this area and is updated to include new chapters on multilevel models with mixed response types, smoothing and multilevel data, models with correlated random effects and modeling with variance.

This book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for many response modeling problems. Free from computational constraints, realistic and state-of-the-art latent variable response models are considered for complex assessment and survey data to solve real-world problems. The Bayesian framework described provides a unified approach for modeling and inference, dealing with (nondata) prior information and information across multiple data sources. The book discusses methods for analyzing item response data and the complex relationships commonly associated with human response behavior and features " Self-contained introduction to Bayesian item response modeling and a coverage of extending standard models to handle complex assessment data " A thorough overview of Bayesian estimation and testing methods for item response models, where MCMC methods are emphasized " Numerous examples that cover a wide range of application areas, including education, medicine, psychology, and sociology " Datasets and software (S+, R, and WinBUGS code) of the models and methods presented in the book are available on www.jean-paulfox.com Bayesian Item Response Modeling is an excellent book for research professionals, including applied statisticians, psychometricians, and social scientists who analyze item response data from a Bayesian perspective. It is a guide to the growing area of Bayesian response modeling for researchers and graduate students, and will also serve them as a good reference. Jean-Paul Fox is Associate Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His main research activities are in several areas of Bayesian response modeling. Dr. Fox has published numerous articles in the areas of Bayesian item response analysis, statistical methods for analyzing multivariate categorical response data, and nonlinear mixed effects models

Human Capital Systems, Analytics, and Data Mining provides human capital professionals, researchers, and students with a comprehensive and portable guide to human capital systems, analytics and data mining. The main purpose of this book is to provide a rich tool set of methods and tutorials for Human Capital Management Systems (HCMS) database modeling, analytics, interactive dashboards, and data mining that is independent of any human capital software vendor offerings and is equally usable and portable among both commercial and internally developed HCMS. The book begins with an overview of HCMS, including coverage of human resource systems history and current HCMS Computing Environments. It next explores relational and dimensional database management concepts and principles. HCMS Instructional databases developed by the Author for use in Graduate Level HCMS and Compensation Courses are used for database modeling and dashboard design exercises. Exciting knowledge discovery and research Tutorials and Exercises using Online Analytical Processing (OLAP) and data mining tools through replication of actual original pay equity research by the author are included. New findings concerning Gender Based Pay Equity Research through the lens Comparable Worth and Occupational Mobility are covered extensively in Human Capital Metrics, Analytics and Data Mining Chapters.

This volume gathers selected peer-reviewed papers presented at the international conference "MAF 2016 - Mathematical and Statistical Methods for Actuarial Sciences and Finance", held in Paris (France) at the Université Paris-Dauphine from March 30 to April 1, 2016. The contributions highlight new ideas on mathematical and statistical methods in actuarial sciences and finance. The cooperation between mathematicians and statisticians working in insurance and finance is a very fruitful field, one that yields unique theoretical models and practical applications, as well as new insights in the discussion of problems of national and international interest. This volume is addressed to academicians, researchers, Ph. D. students and professionals.

Sortition -- also known as random selection -- puts ordinary people in control of decision-making in government. This may seem novel, but it is how the original Athenian democracy worked. In fact, what is new is our belief that electoral systems are democratic. It was self-evident to thinkers from Aristotle to the Renaissance that elections always resulted in oligarchies, or rule by elites. In this distillation of a lifetime's thinking about the history and principles of democracy, Maurice Pope presents a new model of governance that replaces elected politicians with assemblies selected by lot. The re-introduction of sortition, he believes, offers a way out of gridlock, apathy, alienation and polarisation by giving citizens back their voice. Pope's work -- published posthumously -- grew from his unique perspective as a widely travelled English classicist who also experienced the injustice of apartheid rule in South Africa. His great mind was as much at home with the history of philosophy as the mathematics of probability. Governments and even the EU have tried out sortition in recent years; the UK, France and several countries have attempted to tackle climate change through randomly selected citizens' assemblies. The city of Paris and the German-speaking community of Belgium have set up permanent upper houses chosen by lot. Several hundred such experiments around the world are challenging the assumption that elections are the only or ideal route to credible, effective government. Writing before these mostly advisory bodies took shape, Pope lays out a vision for a government entirely based on random selection and citizen deliberation. In arguing for this more radical goal, he draws on the glories of ancient Athens, centuries of use in Venice, the success of randomly selected juries and the philosophical advantages of randomness. Sortition-based democracy, he believed, is the only plausible way to achieve each element of Abraham Lincoln's call for a democratic government "of the people, by the people, for the people". --

This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields.The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes.This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields

The introduction to this book will be short and related only to this volume. The market is either trending or in a trading range and it is often transitioning from one to the other. When the market is transitioning from a trading range into a trend, it is breaking out. Since trends were just discussed in the first book, this second book begins with how trading ranges turn into trends, which are now familiar to the reader. It explains why breakouts form and why they end, which is always at some kind of support or resistance area. The market gets drawn quickly to these areas and because of this pull, I refer to them as magnets. As the breakout is unfolding, traders can use several mathematical techniques to measure where the trend will likely end and then begin to form a trading range, and these measured moves are discussed in detail. Once the market reaches a magnet, it then pauses and pulls back, and usually then resumes. Pullbacks are reliable setups and the book describes them and how to trade them in detail. If a pullback grows so large that it is uncertain if the trend will resume or reverse, it has become a trading range. Most markets are in trading ranges most of the time and therefore most trades that traders make are within trading ranges. Understanding them and how to trade them is critical to anyone trying to make a living as a trader. Traders need to know how to place orders to get into and out of trades and it is useful to know how to scale into and out of positions. Also, mathematics is the basis for all trading. Every trader asks himself, "Will I make money if I take this trade?" This means that the traders is making a statistical analysis of what he sees based on risk, reward, and probability, and understanding this math makes trading less stressful and more profitable.

"The introduction to this book will be short and related only to this volume. The market is either trending or in a trading range and it is often transitioning from one to the other. When the market is transitioning from a trading range into a trend, it is breaking out. Since trends were just discussed in the first book, this second book begins with how trading ranges turn into trends, which are now familiar to the reader. It explains why breakouts form and why they end, which is always at some kind of support or resistance area. The market gets drawn quickly to these areas and because of this pull, I refer to them as magnets. As the breakout is unfolding, traders can use several mathematical techniques to measure where the trend will likely end and then begin to form a trading range, and these measured moves are discussed in detail. Once the market reaches a magnet, it then pauses and pulls back, and usually then resumes. Pullbacks are reliable setups and the book describes them and how to trade them in detail. If a pullback grows so large that it is uncertain if the trend will resume or reverse, it has become a trading range. Most markets are in trading ranges most of the time and therefore most trades that traders make are within trading ranges. Understanding them and how to trade them is critical to anyone trying to make a living as a trader. Traders need to know how to place orders to get into and out of trades and it is useful to know how to scale into and out of positions. Also, mathematics is the basis for all trading. Every trader asks himself, "Will I make money if I take this trade?" This means that the traders is making a statistical analysis of what he sees based on risk, reward, and probability, and understanding this math makes trading less stressful and more profitable"--