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| multivariate models and dependence concepts: Multivariate Models and Multivariate Dependence Concepts Harry Joe, 1997-05-01 This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems. |
| multivariate models and dependence concepts: Multivariate Models and Dependence Concepts Harry Joe, 1997 |
| multivariate models and dependence concepts: Multivariate Models and Multivariate Dependence Concepts Harry Joe, 1997 This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems. |
| multivariate models and dependence concepts: Dependence Modeling: Vine Copula Handbook Dorota Kurowicka, Harry Joe, 2010-12-23 This book is a collaborative effort from three workshops held over the last three years, all involving principal contributors to the vine-copula methodology. Research and applications in vines have been growing rapidly and there is now a growing need to collate basic results, and standardize terminology and methods. Specifically, this handbook will (1) trace historical developments, standardizing notation and terminology, (2) summarize results on bivariate copulae, (3) summarize results for regular vines, and (4) give an overview of its applications. In addition, many of these results are new and not readily available in any existing journals. New research directions are also discussed. |
| multivariate models and dependence concepts: Copula Theory and Its Applications Piotr Jaworski, Fabrizio Durante, Wolfgang Karl Härdle, Tomasz Rychlik, 2010-07-16 Copulas are mathematical objects that fully capture the dependence structure among random variables and hence offer great flexibility in building multivariate stochastic models. Since their introduction in the early 50's, copulas have gained considerable popularity in several fields of applied mathematics, such as finance, insurance and reliability theory. Today, they represent a well-recognized tool for market and credit models, aggregation of risks, portfolio selection, etc. This book is divided into two main parts: Part I - Surveys contains 11 chapters that provide an up-to-date account of essential aspects of copula models. Part II - Contributions collects the extended versions of 6 talks selected from papers presented at the workshop in Warsaw. |
| multivariate models and dependence concepts: Introduction to Probability Narayanaswamy Balakrishnan, Markos V. Koutras, Konstadinos G. Politis, 2021-11-24 INTRODUCTION TO PROBABILITY Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite. This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory. A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text’s computer exercises Features applications representing worldwide situations and processes Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena. |
| multivariate models and dependence concepts: An Introduction to Copulas Roger B. Nelsen, 2013-03-09 Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With nearly a hundred examples and over 150 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of Proofs Without Words: Exercises in Visual Thinking, published by the Mathematical Association of America. |
| multivariate models and dependence concepts: Number-Theoretic Methods in Statistics Kai-Tai Fang, Y. Wang, 1993-12-01 This book is a survey of recent work on the application of number theory in statistics. The essence of number-theoretic methods is to find a set of points that are universally scattered over an s-dimensional unit cube. In certain circumstances this set can be used instead of random numbers in the Monte Carlo method. The idea can also be applied to other problems such as in experimental design. This book will illustrate the idea of number-theoretic methods and their application in statistics. The emphasis is on applying the methods to practical problems so only part-proofs of theorems are given. |
| multivariate models and dependence concepts: Dependence Modeling with Copulas Harry Joe, 2014-06-26 Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured facto |
| multivariate models and dependence concepts: Analyzing Dependent Data with Vine Copulas Claudia Czado, 2019 This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications. It focuses on statistical estimation and selection methods for vine copulas in data applications. These flexible copula models can successfully accommodate any form of tail dependence and are vital to many applications in finance, insurance, hydrology, marketing, engineering, chemistry, aviation, climatology and health. The book explains the pair-copula construction principles underlying these statistical models and discusses how to perform model selection and inference. It also derives simulation algorithms and presents real-world examples to illustrate the methodological concepts. The book includes numerous exercises that facilitate and deepen readers understanding, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch. In closing, the book provides insights into recent developments and open research questions in vine copula based modeling. The book is intended for students as well as statisticians, data analysts and any other quantitatively oriented researchers who are new to the field of vine copulas. Accordingly, it provides the necessary background in multivariate statistics and copula theory for exploratory data tools, so that readers only need a basic grasp of statistics and probability. |
| multivariate models and dependence concepts: Multivariate Generalized Linear Mixed Models Using R Damon Mark Berridge, Robert Crouchley, 2011-04-25 Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.A Un |
| multivariate models and dependence concepts: Continuous Bivariate Distributions N. Balakrishnan, Chin Diew Lai, 2009-05-31 Along with a review of general developments relating to bivariate distributions, this volume also covers copulas, a subject which has grown immensely in recent years. In addition, it examines conditionally specified distributions and skewed distributions. |
| multivariate models and dependence concepts: The Methodology and Practice of Econometrics Jennifer Castle, Neil Shephard, 2009-04-30 Building upon, and celebrating the work of David Hendry, this volume consists of a number of specially commissioned pieces from some of the leading econometricians in the world. It reflects on the recent advances in econometrics and considers the future progress for the methodology of econometrics. |
| multivariate models and dependence concepts: Robustness in Econometrics Vladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh, 2017-02-11 This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations. |
| multivariate models and dependence concepts: Analysis of Mixed Data Alexander R. de Leon, Keumhee Carrière Chough, 2013-01-16 A comprehensive source on mixed data analysis, Analysis of Mixed Data: Methods & Applications summarizes the fundamental developments in the field. Case studies are used extensively throughout the book to illustrate interesting applications from economics, medicine and health, marketing, and genetics. Carefully edited for smooth readability and |
| multivariate models and dependence concepts: Point Processes for Reliability Analysis Ji Hwan Cha, Maxim Finkelstein, 2018-01-17 Focusing on the theory and applications of point processes, Point Processes for Reliability Analysis naturally combines classical results on the basic and advanced properties of point processes with recent theoretical findings of the authors. It also presents numerous examples that illustrate how general results and approaches are applied to stochastic description of repairable systems and systems operating in a random environment modelled by shock processes. The real life objects are operating in a changing, random environment. One of the ways to model an impact of this environment is via the external shocks occurring in accordance with some stochastic point processes. The Poisson (homogeneous and nonhomogeneous) process, the renewal process and their generalizations are considered as models for external shocks affecting an operating system. At the same time these processes model the consecutive failure/repair times of repairable engineering systems. Perfect, minimal and intermediate (imperfect) repairs are discussed in this respect. Covering material previously available only in the journal literature, Point Processes for Reliability Analysis provides a survey of recent developments in this area which will be invaluable to researchers and advanced students in reliability engineering and applied mathematics. |
| multivariate models and dependence concepts: Statistical Inference, Econometric Analysis and Matrix Algebra Bernhard Schipp, Walter Krämer, 2008-11-27 This Festschrift is dedicated to Götz Trenkler on the occasion of his 65th birthday. As can be seen from the long list of contributions, Götz has had and still has an enormous range of interests, and colleagues to share these interests with. He is a leading expert in linear models with a particular focus on matrix algebra in its relation to statistics. He has published in almost all major statistics and matrix theory journals. His research activities also include other areas (like nonparametrics, statistics and sports, combination of forecasts and magic squares, just to mention afew). Götz Trenkler was born in Dresden in 1943. After his school years in East G- many and West-Berlin, he obtained a Diploma in Mathematics from Free University of Berlin (1970), where he also discovered his interest in Mathematical Statistics. In 1973, he completed his Ph.D. with a thesis titled: On a distance-generating fu- tion of probability measures. He then moved on to the University of Hannover to become Lecturer and to write a habilitation-thesis (submitted 1979) on alternatives to the Ordinary Least Squares estimator in the Linear Regression Model, a topic that would become his predominant ?eld of research in the years to come. |
| multivariate models and dependence concepts: Handbook of Reliability Engineering Hoang Pham, 2006-04-12 An effective reliability programme is an essential component of every product's design, testing and efficient production. From the failure analysis of a microelectronic device to software fault tolerance and from the accelerated life testing of mechanical components to hardware verification, a common underlying philosophy of reliability applies. Defining both fundamental and applied work across the entire systems reliability arena, this state-of-the-art reference presents methodologies for quality, maintainability and dependability. Featuring: Contributions from 60 leading reliability experts in academia and industry giving comprehensive and authoritative coverage. A distinguished international Editorial Board ensuring clarity and precision throughout. Extensive references to the theoretical foundations, recent research and future directions described in each chapter. Comprehensive subject index providing maximum utility to the reader. Applications and examples across all branches of engineering including IT, power, automotive and aerospace sectors. The handbook's cross-disciplinary scope will ensure that it serves as an indispensable tool for researchers in industrial, electrical, electronics, computer, civil, mechanical and systems engineering. It will also aid professional engineers to find creative reliability solutions and management to evaluate systems reliability and to improve processes. For student research projects it will be the ideal starting point whether addressing basic questions in communications and electronics or learning advanced applications in micro-electro-mechanical systems (MEMS), manufacturing and high-assurance engineering systems. |
| multivariate models and dependence concepts: Advancing the Frontiers of Simulation Christos Alexopoulos, David Goldsman, James R. Wilson, 2009-09-18 This Festschrift honors George Samuel Fishman, one of the founders of the eld of computer simulation and a leader of the disciplines of operations research and the management sciences for the past ve decades, on the occasion of his seventieth birthday. The papers in this volume span the theory, methodology, and application of computer simulation. The lead article is appropriately titled “George Fishman’s Professional Career.” In this article we discuss George’s contributions to operations research and the m- agement sciences, with special emphasis on his role in the advancement of the eld of simulation since the 1960s. We also include a brief personal biography together with comments by several individuals about the extraordinary effect that George has had on all his students, colleagues, and friends. Thesecondarticle,titled“AConversationwithGeorgeFishman,”isthetranscript of an extended interview with George that we conducted in October 2007. In the article titled “Computer Intensive Statistical Model Building,” Russell Cheng studies resampling methods for building parsimonious multiple linear regr- sion models so as to represent accurately the behavior of the dependent variable in terms of the smallest possible subset of explanatory (independent) variables. The author shows how bootstrap resampling can be used not only for rapid identi cation of good models but also for ef cient comparison of competing models. |
| multivariate models and dependence concepts: Handbook of Empirical Research on Islam and Economic Life M. Kabir Hassan, 2016-12-30 In Islamic jurisprudence, a comprehensive ethic has been formulated governing how business and commerce should be run, how accountability to God and the community is to be achieved, and how banking and finance is to be arranged. This Handbook examines how well these values are translated into actual performance. It explores whether those holding true to the system are hindered and put at a disadvantage or whether the Islamic institutions have been able to demonstrate that faith-based activities can be rewarding, both economically and spiritually. |
| multivariate models and dependence concepts: Operations Research Proceedings 2010 Bo Hu, Karl Morasch, Stefan Pickl, Markus Siegle, 2011-06-24 This book contains selected papers from the symposium Operations Research 2010 which was held from September 1-3, 2010 at the Universität der Bundeswehr München, Germany. The international conference, which also serves as the annual meeting of the German Operations Research Society (GOR), attracted more than 600 participants from more than thirty countries. The general theme Mastering Complexity focusses on a natural component of the globalization process. Financial markets, traffic systems, network topologies and, last but not least, energy resource management, all contain complex behaviour and economic interdependencies which necessitate a scientific solution. Operations Research is one of the key instruments to model, simulate and analyze such systems. In the process of developing optimal solutions, suitable heuristics and efficient procedures are some of the challenges which are discussed in this volume. |
| multivariate models and dependence concepts: Actuarial Theory for Dependent Risks Michel Denuit, Jan Dhaene, Marc Goovaerts, Rob Kaas, 2006-05-01 The increasing complexity of insurance and reinsurance products has seen a growing interest amongst actuaries in the modelling of dependent risks. For efficient risk management, actuaries need to be able to answer fundamental questions such as: Is the correlation structure dangerous? And, if yes, to what extent? Therefore tools to quantify, compare, and model the strength of dependence between different risks are vital. Combining coverage of stochastic order and risk measure theories with the basics of risk management and stochastic dependence, this book provides an essential guide to managing modern financial risk. * Describes how to model risks in incomplete markets, emphasising insurance risks. * Explains how to measure and compare the danger of risks, model their interactions, and measure the strength of their association. * Examines the type of dependence induced by GLM-based credibility models, the bounds on functions of dependent risks, and probabilistic distances between actuarial models. * Detailed presentation of risk measures, stochastic orderings, copula models, dependence concepts and dependence orderings. * Includes numerous exercises allowing a cementing of the concepts by all levels of readers. * Solutions to tasks as well as further examples and exercises can be found on a supporting website. An invaluable reference for both academics and practitioners alike, Actuarial Theory for Dependent Risks will appeal to all those eager to master the up-to-date modelling tools for dependent risks. The inclusion of exercises and practical examples makes the book suitable for advanced courses on risk management in incomplete markets. Traders looking for practical advice on insurance markets will also find much of interest. |
| multivariate models and dependence concepts: Handbook of Volatility Models and Their Applications Luc Bauwens, Christian M. Hafner, Sebastien Laurent, 2012-03-22 A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels. |
| multivariate models and dependence concepts: Collateralized debt obligations Markus Lorenz, 2008-10-22 Inhaltsangabe:Abstract: This work aims to give the reader a holistic introduction to Collateralized Debt Obligations (CDOs), an asset category which has recently experienced both popularity and criticism. Collateralized Debt Obligations represent a subset of asset-backed securities. As opposed to classical types of asset-backed-securities like mortgage-backed securities or credit card debt-backed securities, a Collateralized Debt Obligation is a vehicle transforming bank loans or commercial paper into tranches of traded securities. While Collateralized Debt Obligations have been an established part of the U.S. fixed income market, it was only recently that academics showed interest in this asset category. From an asset pricing standpoint, CDOs represent a challenge as credit risk from a heterogeneous pool is passed through to tranches. Hence, asset pricing models have to account for expected defaults and default correlation on the one hand while incorporating the structural support the CDO is offering to the debt tranches on the other. Also, regulatory agencies such as the Basel Committee on Banking Supervision have increasingly covered CDOs and their use in credit risk management, thus further stimulating interest in this asset category. The report is mainly organized in three parts. The first part presents the basic ideas of Collateralized Debt Obligation as well as their structure and principal economics. Part II is the core of the report focusing on the aforementioned asset pricing problem and presenting various models to cope with it. Finally, the third part presents some of the multifaceted applications of Collateral Debt Obligations and concludes with an outlook for the product category. Here, special focus is laid on the European and German market as this is seen as a major area for growth. Inhaltsverzeichnis:Table of Contents: Index of figuresv Index of tablesvi Prefacevii 1.INTRODUCTION1 1.1Definitions1 1.2Mathematical Classification2 1.3Purpose and Relevance of CDOs4 1.4Motivation and Aim of the Study6 2.STRUCTURE AND DESIGN OF CDOS8 2.1Underlying Assets9 2.2Tranches10 2.3Purpose11 2.3.1Risk Transfer11 2.3.2Credit Risk Pricing Arbitrage11 2.4Credit Structure13 2.4.1Market Value Structure13 2.4.2Cash Flow Structure13 2.5Summary and Typical CDO Structures15 3.RATIONALE AND ECONOMIC FEATURES18 3.1Incentives to enter CDO Contracts19 3.1.1Comparative Advantages in Holding Specific Risks19 3.1.2Incentives for Equity [...] |
| multivariate models and dependence concepts: Innovations in Classification, Data Science, and Information Systems Daniel Baier, Klaus-Dieter Wernecke, 2004-11-19 The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques. |
| multivariate models and dependence concepts: From Probability to Finance Ying Jiao, 2020-03-20 This volume presents a collection of lecture notes of mini-courses taught at BICMR Summer School of Financial Mathematics, from May 29 to June 9, 2017. Each chapter is self-contained and corresponds to one mini-course which deals with a distinguished topic, such as branching processes, enlargement of filtrations, Hawkes processes, copula models and valuation adjustment analysis, whereas the global topics cover a wide range of advanced subjects in financial mathematics, from both theoretical and practical points of view. The authors include world-leading specialists in the domain and also young active researchers. This book will be helpful for students and those who work on probability and financial mathematics. |
| multivariate models and dependence concepts: Copulas and Its Application in Hydrology and Water Resources Lu Chen, Shenglian Guo, 2018-06-28 This book presents an overview of copula theory and its application in hydrology, and provides valuable insights, useful methods and practical applications for multivariate hydrological analysis using copulas. In addition, it extends the traditional bivariate model to trivariate or multivariate models. The specific applications covered include the study of flood frequency analysis, drought frequency analysis, dependence analysis, flood coincidence risk analysis and statistical simulation using copulas. The book offers a valuable guide for researchers, scientists and engineers working in hydrology and water resources, and will also benefit graduate or doctoral students with a basic grasp of copula functions who want to learn about the latest research developments in the field. |
| multivariate models and dependence concepts: Risk Measures with Applications in Finance and Economics Michael McAleer, Wing-Keung Wong, 2019-07-23 Risk measures play a vital role in many subfields of economics and finance. It has been proposed that risk measures could be analysed in relation to the performance of variables extracted from empirical real-world data. For example, risk measures may help inform effective monetary and fiscal policies and, therefore, the further development of pricing models for financial assets such as equities, bonds, currencies, and derivative securities.<false,>A Special Issue of “Risk Measures with Applications in Finance and Economics” will be devoted to advancements in the mathematical and statistical development of risk measures with applications in finance and economics. This Special Issue will bring together the theory, practice and real-world applications of risk measures. This book is a collection of papers published in the Special Issue of “Risk Measures with Applications in Finance and Economics” for Sustainability in 2018. |
| multivariate models and dependence concepts: Handbook of Discrete-Valued Time Series Richard A. Davis, Scott H. Holan, Robert Lund, Nalini Ravishanker, 2016-01-06 Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca |
| multivariate models and dependence concepts: Mathematical Analysis and Computing R. N. Mohapatra, S. Yugesh, G. Kalpana, C. Kalaivani, 2021-05-05 This book is a collection of selected papers presented at the International Conference on Mathematical Analysis and Computing (ICMAC 2019) held at Sri Sivasubramaniya Nadar College of Engineering, Chennai, India, from 23–24 December 2019. Having found its applications in game theory, economics, and operations research, mathematical analysis plays an important role in analyzing models of physical systems and provides a sound logical base for problems stated in a qualitative manner. This book aims at disseminating recent advances in areas of mathematical analysis, soft computing, approximation and optimization through original research articles and expository survey papers. This book will be of value to research scholars, professors, and industrialists working in these areas. |
| multivariate models and dependence concepts: Semiparametric Odds Ratio Model and Its Applications Hua Yun Chen, 2021-12-20 Beginning with familiar models and moving onto advanced semiparametric modelling tools Semiparametric Odds Ratio Model and its Applications introduces readers to a new range of flexible statistical models and provides guidance on their application using real data examples. This books range of real-world examples and exploration of common statistical problems makes it an invaluable reference for research professionals and graduate students of biostatistics, statistics, and other quantitative fields. Key Features: Introduces flexible statistical models that have yet to systematically introduced in course materials. Discusses applications of the proposed modelling framework in several important statistical problems, ranging from biased sampling designs and missing data, graphical models, survival analysis, Gibbs sampler and model compatibility, and density estimation. Includes real data examples to demonstrate the use of the proposed models, and estimation and inference tools. |
| multivariate models and dependence concepts: Mathematical and Statistical Methods in Reliability Bo Lindqvist, Kjell A. Doksum, 2003 This book contains extended versions of carefully selected and reviewed papers presented at the Third International Conference on Mathematical Methods in Reliability, held in Norway in 2002. It provides an overview of current research activities in reliability theory. The authors are all leading experts in the field. Readership: Graduate students, academics and professionals in probability & statistics, reliability analysis, survival analysis, industrial engineering, software engineering, operations research and applied mathematics research. |
| multivariate models and dependence concepts: From Stochastic Calculus to Mathematical Finance Yu. Kabanov, R. Liptser, J. Stoyanov, 2007-04-03 Dedicated to the Russian mathematician Albert Shiryaev on his 70th birthday, this is a collection of papers written by his former students, co-authors and colleagues. The book represents the modern state of art of a quickly maturing theory and will be an essential source and reading for researchers in this area. Diversity of topics and comprehensive style of the papers make the book attractive for PhD students and young researchers. |
| multivariate models and dependence concepts: Extreme Values in Finance, Telecommunications, and the Environment Barbel Finkenstadt, Holger Rootzen, 2003-07-28 Because of its potential to ...predict the unpredictable,... extreme value theory (EVT) and methodology is currently receiving a great deal of attention from statistical and mathematical researchers. This book brings together world-recognized authorities in their respective fields to provide expository chapters on the applications, use, and theory |
| multivariate models and dependence concepts: Time Series Analysis and Forecasting Ignacio Rojas, Héctor Pomares, Olga Valenzuela, 2018-10-03 This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics. |
| multivariate models and dependence concepts: Fundamental Aspects of Operational Risk and Insurance Analytics Marcelo G. Cruz, Gareth W. Peters, Pavel V. Shevchenko, 2015-02-23 A one-stop guide for the theories, applications, and statistical methodologies essential to operational risk Providing a complete overview of operational risk modeling and relevant insurance analytics, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk offers a systematic approach that covers the wide range of topics in this area. Written by a team of leading experts in the field, the handbook presents detailed coverage of the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements. Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk begins with coverage on the four data elements used in operational risk framework as well as processing risk taxonomy. The book then goes further in-depth into the key topics in operational risk measurement and insurance, for example diverse methods to estimate frequency and severity models. Finally, the book ends with sections on specific topics, such as scenario analysis; multifactor modeling; and dependence modeling. A unique companion with Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk, the handbook also features: Discussions on internal loss data and key risk indicators, which are both fundamental for developing a risk-sensitive framework Guidelines for how operational risk can be inserted into a firm’s strategic decisions A model for stress tests of operational risk under the United States Comprehensive Capital Analysis and Review (CCAR) program A valuable reference for financial engineers, quantitative analysts, risk managers, and large-scale consultancy groups advising banks on their internal systems, the handbook is also useful for academics teaching postgraduate courses on the methodology of operational risk. |
| multivariate models and dependence concepts: Statistical Modeling Using Local Gaussian Approximation Dag Tjøstheim, Håkon Otneim, Bård Støve, 2021-10-05 Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the copula, Applications in finance, and more. Additional chapters explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation, Conditional density estimation, The local Gaussian partial correlation, Regression and conditional regression quantiles, and a A local Gaussian Fisher discriminant. - Reviews local dependence modeling with applications to time series and finance markets - Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics - Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences - Integrates textual content with three useful R packages |
| multivariate models and dependence concepts: Financial Data Analytics with Machine Learning, Optimization and Statistics Sam Chen, Ka Chun Cheung, Phillip Yam, 2024-10-21 An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking. |
| multivariate models and dependence concepts: The New Palgrave Dictionary of Economics , 2016-05-18 The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product. |
| multivariate models and dependence concepts: Introduction to Agent-Based Economics Mauro Gallegati, Antonio Palestrini, Alberto Russo, 2017-08-03 Introduction to Agent-Based Economics describes the principal elements of agent-based computational economics (ACE). It illustrates ACE's theoretical foundations, which are rooted in the application of the concept of complexity to the social sciences, and it depicts its growth and development from a non-linear out-of-equilibrium approach to a state-of-the-art agent-based macroeconomics. The book helps readers gain a better understanding of the limits and perspectives of the ACE models and their capacity to reproduce economic phenomena and empirical patterns. - Reviews the literature of agent-based computational economics - Analyzes approaches to agents' expectations - Covers one of the few large macroeconomic agent-based models, the Modellaccio - Illustrates both analytical and computational methodologies for producing tractable solutions of macro ACE models - Describes diffusion and amplification mechanisms - Depicts macroeconomic experiments related to ACE implementations |
Multivariate Models and Dependence Concepts
Multivariate Models and Dependence Concepts Contents Preface xv 1 …
Joe, Harry. Multivariate Models and Dependence Concepts. (1997)
Extreme value copulas with generalized extreme value univariate survival margins …
Quantitative Risk Management: Concepts, Techniques and Tools
6 Multivariate Models 173 6.1 Basics of Multivariate Modelling 174 6.1.1 Random …
Multivariate Models And Dependence Concepts [PDF]
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MODELS FOR CONSTRUCTION OF MULTIVARIATE DEPENDENCE - A C…
A multivariate data set, which exhibit complex patterns of dependence, particularly in the …
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Joe, H., Multivariate models and dependence concepts. 1997, Boca Raton: Chapman and …
GENERAL MULTIVARIATE DEPENDENCE USING ASSOCIATED …
Although much has been written on the need to understand multivariate non-positive …
Models for construction of multivariate dependence
KEY WORDS: Multivariate models, Nested Archimedean copulas, Pair-copula …
Multivariate Models and Dependence Concepts
Multivariate Models and Dependence Concepts Contents Preface xv 1 Introduction 1 1.1 Overview and background 2 1.2 Style and format 5 1.3 Notation, abbreviations and conventions 7 1.4 …
Joe, Harry. Multivariate Models and Dependence Concepts.
Extreme value copulas with generalized extreme value univariate survival margins are models for multivariate minima. Some (but not all!) families of extreme value copulas are obtained as the …
Quantitative Risk Management: Concepts, Techniques and …
6 Multivariate Models 173 6.1 Basics of Multivariate Modelling 174 6.1.1 Random Vectors and Their Distributions 174 ... 7.2 Dependence Concepts and Measures 235 7.2.1 Perfect Dependence 236 …
Multivariate Models And Dependence Concepts [PDF]
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MODELS FOR CONSTRUCTION OF MULTIVARIATE DEPENDENCE …
A multivariate data set, which exhibit complex patterns of dependence, particularly in the tails, can be modelled using a cascade of lower-dimensional copulae. In this paper, we compare two such …
Multivariate dependence and genetic networks inference
Joe, H., Multivariate models and dependence concepts. 1997, Boca Raton: Chapman and Hall.. For metric variables and simple constraints, these classes are well studied. We know parametric …
GENERAL MULTIVARIATE DEPENDENCE USING ASSOCIATED COPULAS
Although much has been written on the need to understand multivariate non-positive tail dependence, no formal definition has been presented. In this work we define the necessary …
Models for construction of multivariate dependence
KEY WORDS: Multivariate models, Nested Archimedean copulas, Pair-copula decompositions ∗PhD-student University of Oslo and Norwegian Computing Center, daniel@danielberg.no †Assistant …
Note Set 2, Multivariate Probability Models - Donald Bren School …
This set of notes will cover basic concepts in multivariate probability models, i.e., probability models involving multiple random variables. We will begin by discussing the important concepts of …
Multivariate Models for Operational Risk - ResearchGate
Keywords: dependence model, L´evy copula, multivariate dependence, multivariate L´evy processes, operational risk, Pareto distribution, regular variation, subexponential distri-bution 1 …
Modeling Statistical Dependence - Purdue University
For the modeling/characterization of statistical dependence, di erent concepts/tools are needed. Among important issues is the ordering of dependence, and among important modeling tools are …
Models for construction of multivariate dependence: A …
It allows for the free specification of d−1 copulae and corresponding distributional parameters, while the remaining 2)/2 copulae and parameters are implicitly given through the construction. More …
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Introduction Preliminaries NestedArchimedeanconstructions Pair-copulaconstructions Comparison Applications Summary Introduction. ApartfromtheGaussian …
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Note Set 2: Conditional Independence and Graphical Models
This set of notes will cover basic concepts in multivariate probability models, i.e., probability models involving multiple random variables. We will begin by discussing the important concepts of …
Chapter Basic Concepts for Multivariate Statistics
Basic Concepts for Multivariate Statistics. Chapter. 1. 1.1 Introduction 1. 1.2 Population Versus Sample 2. 1.3 Elementary Tools for Understanding Multivariate Data. 1.4 Data Reduction, …
Chapter 1: A detailed overview of multivariate methods
The basic concepts • The variate = A linear combination of variables with empirically determined weights. Variables are determined by the researcher, the weights by the multivariate technique. …
Non-Gaussian Multivariate Statistical Models and their Applications
las. In applications in finance and insurance, models with more dependence in the joint tails than multivariate non-Gaussian are important. This explains why copula models which can allow for …
