Bøger af David Ruppert

<div style="e;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal"e;>Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook <em>Statistics and Finance: An Introduction</em>, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. </div><div style="e;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal"e;>The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div><div style="e;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal"e;>Some exposure to finance is helpful.</div>

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A review of the major statistical techniques which can be used to analyze regression data with nonconstant variability and skewness. The authors have developed techniques to deal with these types of problems, the complications of which can be observed in diverse fields. Annotation copyright Book New

This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts.

This book emphasizes the applications of statistics and probability to finance. The book covers the classical methods of finance and it introduces the newer area of behavioral finance.