First published in 1972, Distribution Theory follows on from the author's earlier book, Descriptive Statistics and Probability Theory, but may easily be followed by any reader who has not studied that particular book but who has gained some knowledge of numerical distributions and basic probability theory.
The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference.
First published in 1972, in Descriptive Statistics and Probability Theory the numerical work- the selection of numerical data-is used as a basis for developing the statistical theory.
First published in 1972, in Descriptive Statistics and Probability Theory the numerical work- the selection of numerical data-is used as a basis for developing the statistical theory.
Risk Analysis in Finance and Insurance, Third Edition presents an accessible yet comprehensive introduction to the main concepts and methods that transform risk management into a quantitative science.
This work aims to serve two primary purposes: first, to present findings regarding the age and related characteristics of corporations within the private enterprise system.
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations.
Explores the Origin of the Recent Banking Crisis and how to Preclude Future CrisesShedding new light on the recent worldwide banking debacle, The Banking Crisis Handbook presents possible remedies as to what should have been done prior, during, and after the crisis.
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years.
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled.
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.
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems.
Featuring international contributors from both industry and academia, Numerical Methods for Finance explores new and relevant numerical methods for the solution of practical problems in finance.
This volume comprises selected papers presented at the 12th Winter School on Stochastic Processes and their Applications, which was held in Siegmundsburg, Germany, in March 2000.
Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology.
While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases.
Featuring contributions from leading international academics and practitioners, Credit Risk: Models, Derivatives, and Management illustrates how a risk management system can be implemented through an understanding of portfolio credit risks, a set of suitable models, and the derivation of reliable empirical results.
Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community.
Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk.
Alles, was du schon immer über die Versprechungen der Schönheitsindustrie, über Erfolge der Medizin und all die zukünftigen Verbesserungen unseres Lebens wissen wolltest und manchmal – zu Recht – nicht glauben konntest.
Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes.
More than just an investment dictionary, 101 Investment Tools for Buying Low and Selling High analyzes in a concise style various investment vanes-from stock indexes to measures of affordable housing to leading economic reports.
Until now there were no published analyses of the recent solvency work conducted in Europe, specifically the risk categories proposed by the International Actuarial Association (IAA).
Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures.
Providing the expertise of an internal business consultant to one of the largest issuers of mortgage securities, Investing in Mortgage Securities serves as a high-level introduction to mortgage securities presented within the framework of fixed income securities.
