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#### Displaying Time Series, Spatial, and Space-Time Data with R

CRC Press 2016; US$ 57.95Code and Methods for Creating High-Quality Data Graphics A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets.... more...

#### Time Series with Mixed Spectra

CRC Press 2016; US$ 57.95Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the statistical... more...

#### The Analysis of Time Series

CRC Press 2016; US$ 87.95Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting... more...

#### Models for Dependent Time Series

CRC Press 2015; US$ 57.95Models 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. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector)... more...

#### Time Series Modelling with Unobserved Components

CRC Press 2015; US$ 57.95Despite 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. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical... more...

#### Mathematicians under the Nazis

Princeton University Press 2014; Not AvailableContrary to popular belief--and despite the expulsion, emigration, or death of many German mathematicians--substantial mathematics was produced in Germany during 1933-1945. In this landmark social history of the mathematics community in Nazi Germany, Sanford Segal examines how the Nazi years affected the personal and academic lives of those German... more...

#### André-Louis Cholesky

Springer International Publishing 2014; US$ 96.11This book traces the life of Cholesky (1875-1918), and gives his family history. After an introduction to topography, an English translation of an unpublished paper by him where he explained his method for linear systems is given, studied and replaced in its historical context. His other works, including two books, are also described as well as his... more...

#### Basic Data Analysis for Time Series with R

Wiley 2014; US$ 119.00 US$ 107.10Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic... more...

#### Hidden Markov Models in Finance

Springer US 2014; US$ 101.76This book offers cutting-edge research developments and applications of Hidden Markov Models (HMMs) to finance and closely allied fields. It will help readers to use HMMs to accurately and efficiently capture many of the processes in the financial market. more...

#### The Spectral Analysis of Time Series

Elsevier Science 2014; US$ 72.95The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines... more...