ISBN: 978-1-119-21466-3
Oct 2018
464 pages
Hardcover
A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R.
Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications.
Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book.
Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates.
Explains both the use and theoretical justification of robust methods
Guides readers in selecting and using the most appropriate robust methods for their problems
Features computational algorithms for the core methods
Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models.
Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.
ISBN: 978-1-119-50471-9
Feb 2019
450 pages
Select type: Hardcover
Introduces the latest developments in forecasting in advanced quantitative data analysis
This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable.
Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers.
Presents models that are all classroom tested
Contains real-life data samples
Contains over 350 equation specifications of various time series models
Contains over 200 illustrative examples with special notes and comments
Applicable for time series data of all quantitative studies
Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
ISBN: 978-1-119-13700-9
Feb 2019
592 pages
Select type: Hardcover
This book focuses on the mathematical models and techniques used in actuarial finance for the pricing and hedging of actuarial liabilities exposed to financial markets and other contingencies. The classical theory of financial mathematics is discussed while covering additional topics of interest for actuaries. Actuarial applications play a pivotal role and actuarial content is integrated throughout. Specifically, insurance liabilities and financial derivatives are described in the first chapters, in addition to valuation principles that differ in financial and insurance markets. Classical books in financial mathematics focus on pricing options and futures in absence of arbitrage whereas actuarial finance mainly involves valuation of liabilities tied to financial markets and risk management using derivatives. Therefore, this book devotes entire chapters or sections to topics of greater importance for actuaries such as the management of mortality risk and other non-tradable risks in the industry; valuation and reserving modern insurance liabilities that involve understanding the differences between the real-world and risk-neutral probability measures; and stochastic interest rates in discrete- and continuous-time given the long-term nature of insurance liabilities. The authors clearly differentiate the real-world and risk-neutral probability measures and also provide exercises, select solutions, and R data sets for additional learning.
ISBN: 978-1-119-24546-9
Mar 2019
576 pages
Select type: Hardcover
Maintaining the clear writing style and effective pedagogical approach of the prior edition, the Second Edition features new coverage on many topics, including preconditioning, kriging methods designed for stochastic data, interpolation in two and three dimensions, steady-state problems, and finite difference methods for variable-coefficient elliptic equations. This new edition also presents expanded coverage on both the finite-element method and multigrid methods. The authors present an introduction to numerical analysis and numerical methods and discuss the various applications within the fields of applied mathematics, engineering, and the physical and life sciences. Including an in-depth exploration of the basic theoretical results and proofs in numerical analysis, this book combines theory and practice and provides a broad selection of current numerical methods that specifically emphasize scientific computation involving differential equations. This new edition provides updated coverage of topics often omitted in similar titles at this level, which includes multidimensional interpolation, Quasi-Newton methods, multigrid method, QR methods of eigenvalues, finite elements, and partial differential equations. A section on useful tools such as bounded sets, normed linear spaces, and calculus results is provided, and the necessary background material needed for studying numerical analysis is presented. In addition, this book uniquely introduces the motivation and construction behind these numerical methods in order to explain the types of problems being addressed and the heuristic ideas behind the concepts. Practical considerations are included to facilitate the translation of concepts into computer code, and the presented mathematical analysis establishes the underlying theory and analytic techniques commonly used to prove numerical methods. This book also details the advanced theory and complexity behind these topics with references for further study.
ISBN: 978-1-119-50285-2
Mar 2019
680 pages
Select type: Hardcover
An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field
Following the highly successful and much lauded book, Time Series Analysis?Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series.
With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis.
Written by bestselling author and leading expert in the field
Covers topics not yet explored in current multivariate books
Features classroom tested material
Written specifically for time series courses
Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.