New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: <ul>
<li>Contributions from eleven of the worldâ??s leading figures in time series</li> <li>Shared balance between theory and application</li> <li>Exercise series sets</li> <li>Many real data examples</li> <li>Consistent style and clear, common notation in all contributions</li> <li>60 helpful graphs and tables</li> </ul>
Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

A Course in Time Series Analysis

Science & Math

Tsay, Ruey S.

Tiao, George C.

Peña, Daniel

Mathematics

Wiley-Interscience

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data.&nbsp; Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests.&nbsp; Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given.&nbsp; The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: <ul> <li>New ways to plot large sets of time series</li> <li>An automatic procedure to build univariate ARMA models for individual components of a large data set</li> <li>Powerful outlier detection procedures for large sets of related time series</li> <li>New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series</li> <li>Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models</li> <li>Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series</li> <li>Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.</li> <li>Introduction of modern procedures for modeling and forecasting spatio-temporal data&nbsp;</li> </ul> Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics)

wiley

Vividly portraying the symbiotic relationship between American immigration policy and the drug war in Mexico,Bang is a meditation on the loss of talented individuals and the black market machines fed with the people removed from and shut out of America.

Bang

Literature & Fiction

United States

Arte Publico Press

Este libro comienza con los análisis descriptivos más simples de series temporales, presenta los métodos actuales para construir modelos dinámicos y obtener predicciones y discute los problemas que constituyen las fronteras de la investigación actual en series temporales. Puede servir de texto para un curso de predicción di

Análisis de series temporales (Manuales / Manual) (Spanish Edition)

Politics & Social Sciences

Sociology

Alianza Editorial

Fundamentos de Estadística (Spanish Edition)

Regresion y diseno de experimentos / Regression and experimental design (El Libro Universitario. Manuales) (Spanish Edition)

Arts & Photography

Drawing

Alianza Editorial Sa

Analisis multivariante de datos

McGraw-Hill Interamericana de Espana, SL

<ul>
<li>The rediscovery of the stunning work by an American top photographer</li>
<li>John G. Zimmerman (1927-2002) is a true icon of American photography. For decades his pictures appeared on the covers of magazines like Time, Life and Sports Illustrated</li>
<li>His massive oeuvre covers a diversity of fields, from the arts, sports and fashion to politics and Black America. The photographs in this publication present an enduring image of the United States from the 1950s to the 1970s</li>
</ul>
This book reveals the stunning early work of John G. Zimmerman (1927-2002), a true icon of American photography. His massive oeuvre gives a unique panorama of American life and culture in the second half of the twentieth century. This publication presents a selection of black-and-white pictures, shot between 1950 and 1978, which show the rough and uncut visual ingenuity of Zimmerman.
Zimmerman was a man of many facets and his pictures cover a diversity of subjects, ranging from sports, fashion, arts and architecture to politics and the Jim Crow South of the 1950s. From producing whimsical ads to covering exclusive stories for mainstream magazines such as Life, Time, Sports Illustrated and Paris Match, from photographing the Beatles to shooting the disastrous fire at a General Motors plant... Zimmerman could do it all and did it - always pushing the boundaries of the photographic medium. In his endless search for a new visual language and the essence of movement, he created whatever he needed to make the seemingly impossible possible.
John G. Zimmerman was a refined chronicler, who wanted to depict every sphere of society, but above all wanted to see, to see more, to see it all.

America in Black & White: Selected Photographs of John G. Zimmerman

De Winde, Arne

Kohns, Oliver

Hodgson, Francis

Photography & Video

Cannibal Publishing

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