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This volume is a handbook for enterprise system developers, guiding them through the intricacies and lessons learned in enterprise application development. It provides proven solutions to the everyday problems facing information systems developers.
The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web Resource A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.
Paul Wilmott Introduces Quantitative Finance, Second Edition is an accessible introduction to the classical side of quantitative finance specifically for university students. Adapted from the comprehensive, even epic, works Derivatives and Paul Wilmott on Quantitative Finance, Second Edition, it includes carefully selected chapters to give the student a thorough understanding of futures, options and numerical methods. Software is included to help visualize the most important ideas and to show how techniques are implemented in practice. There are comprehensive end-of-chapter exercises to test students on their understanding.
Millions of traders participating in today’s financial markets have shot interest and involvement in technical analysis to an all-time high. This updated edition of Technical Analysis from A to Z combines a detailed explanation of what technical analysis is and how it works with overviews, interpretations, calculations, and examples of over 135 technical indicators—and how they perform under actual market conditions. Enhanced with more details to make it easier to use and understand, this book reflects the latest research findings and advances. A complete summary of major indicators that can be used in any market, it covers: • Every trading tool from the Absolute Breadth Index to the Zig Zag • Indicators include Arms Index, Dow Theory, and Elliott Wave Theory • Over 35 new indicators
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available. The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
James Douglas Hamilton
Difference equations; Lag operators; Stationary ARMA processes; Forecasting; Maximum likelihood estimation; Spectral analysis; Asymptotic distribution theory; Linear regression models; Linear systems of simultaneous equations; Covariance-stationary vector processes; Vector autoregressions; Bayesian analysis; The Kalman Filter; Generalized method of moments; Models of nonstationary time series; Processes with deterministic time trends; Univariate processes with unit roots; Unit roots in multivariate time series; Cointegration; Full-information maximum likelihood analysis of cointegrated systems; Time series models of heteroskedasticity; Modeling time series with changes in regime; Mathematical review; Statistical tables; Answers to selected exercises; Greek letters and mathematical symbols used in the text.