An update of one of the most trusted books on constructing and analyzing actuarial models for the C/4 actuarial examThis new, abridged edition has been thoroughly revised and updated to include the essential material related to Exam C of the Society of Actuaries' and Casualty Actuarial Society's accreditation programs. The book maintains an approach to modeling and forecasting that utilizes tools related to risk theory, loss distributions, and survival models. Random variables, basic distributional quantities, the recursive method, and techniques for classifying and creating distributions are also discussed. Both parametric and non-parametric estimation methods are thoroughly covered along with advice for choosing an appropriate model.
The book continues to distinguish itself by providing over 400 exercises that have appeared on previous examinations. The emphasis throughout is now placed on calculations and spreadsheet implementation. Additional features of the Fourth Edition include: extended discussions of risk management and risk measures, including Tail-Value-at-Risk; expanded coverage of copula models and their estimation; new sections on extreme value distributions and their estimations, compound frequency class of distributions, and estimation for the compound class; and motivating examples from fields of insurance and business. All data sets are available on an FTP site. An assortment of supplements (both print and electronic) is available.
Loss Models, Fourth Edition is an essential resource for students and aspiring actuaries who are preparing to take the SOA and CAS preliminary examinations C/4. It is also a must-have reference for professional actuaries, graduate students in the actuarial field, and anyone who works with loss and risk models in their everyday work.
To explore our additional offerings in actuarial exam preparation visit www.wiley.com/go/c4actuarial.
Particulate discrete element analysis is becoming increasingly popular for research in geomechanics as well as geology, chemical engineering, powder technology, petroleum engineering and in studying the physics of granular materials. With increased computing power, practising engineers are also becoming more interested in using this technology for analysis in industrial applications. This is the first single work on Discrete Element Modelling (DEM) providing the information to get started with this powerful numerical modelling approach.
Written by an independent author with experience both in developing DEM codes and using commercial codes, this book provides the basic details of the numerical method and the approaches used to interpret the results of DEM simulations. Providing a basic overview of the numerical method, Particulate Discrete Element Modelling discusses issues related to time integration and numerical stability, particle types, contact modelling and boundary conditions. It summarizes approaches to interpret DEM data so that users can maximize their insight into the material response using DEM. The aim of this book is to provide both users and prospective users of DEM with a concise reference book that includes tips to optimize their usage.
Particulate Discrete Element Modelling is suitable both for first time DEM analysts as well as more experienced users. It will be of use to professionals, researchers and higher level students, as it presents a theoretical overview of DEM as well as practical guidance on running DEM simulations and interpreting DEM simulation data.
This lively and engaging textbook explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The author, David A. Freedman, explains the basic ideas of association and regression, and takes you through the current models that link these ideas to causality. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: - authoritative guidance from a well-known author with wide experience in teaching, research, and consulting - careful analysis of statistical issues in substantive applications - no-nonsense, direct style - versatile structure, enabling the text to be used as a text in a course, or read on its own - text that has been thoroughly class-tested at Berkeley - background material on regression and matrix algebra - plenty of exercises, most with solutions - extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) - many new exercises and examples - reorganized, restructured, and revised chapters to aid teaching and understanding
This book is founded on the idea that becoming is the most useful defining concept for a new professional class whose members understand that development in their working lives is an open-ended, lifelong process of refinement and learning.
In a world where being a professional is an increasingly indistinct notion and where better education and technology are challenging professional norms, it is imperative that we no longer think in terms of an exclusive, Anglo-American, knowledge-rich class of workers. Exploring the implications of this insight for professions including nursing, teaching, social work, engineering and the clergy, this volume aims to encourage informed debate on what it means to be a professional in this globalised 21st century.
The book argues that becoming a professional is a lifelong process in which individual professional identities are constructed through formal education, workplace interactions and popular culture. The book advocates the ongoingness of developing a professional self throughout one s professional life. What emerges is a concept of becoming a professional different from the isolated, rugged, individualistic approach to traditional professional practice as represented in popular culture. It is a book for the reflective professional.
Multiscale modeling is becoming essential for accurate, rapid simulation in science and engineering. This book presents the results of three decades of research on multiscale modeling in process engineering from principles to application, and its generalization for different fields. This book considers the universality of meso-scale phenomena for the first time, and provides insight into the emerging discipline that unifies them, meso-science, as well as new perspectives for virtual process engineering.
Multiscale modeling is applied in areas including:
Jinghai Li is Vice-President of the Chinese Academy of Sciences (CAS), a professor at the Institute of Process Engineering, CAS, and leader of the EMMS (Energy-minimizing multiscale) Group.
Wei Ge, Wei Wang, Ning Yang and Junwu Wang are professors at the EMMS Group, part of the Institute of Process Engineering, CAS.
Xinhua Liu, Limin Wang, Xianfeng He and Xiaowei Wang are associate professors at the EMMS Group, part of the Institute of Process Engineering, CAS.
Mooson Kwauk is an emeritus director of the Institute of Process Engineering, CAS, and is an advisor to the EMMS Group.
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