Name: Change Point Analysis for Time Series (en Inglés)
Price: 117.93 USD
Availability: InStock
Author: Horváth, Lajos ; Rice, Gregory
ISBN: 9783031516085

portada Change Point Analysis for Time Series (en Inglés)

Change Point Analysis for Time Series (en Inglés)

Formato

Libro Físico

Autor

Lajos Horváth

Gregory Rice

Editorial

Springer

Idioma

Inglés

N° páginas

545

Encuadernación

Tapa Dura

ISBN13

9783031516085

Categorías

Probabilidad y estadística
Matemáticas aplicadas
Biología, ciencias de la vida

Change Point Analysis for Time Series (en Inglés)

Lajos Horváth (Autor) · Gregory Rice (Autor) · Springer · Tapa Dura

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Reseña del libro "Change Point Analysis for Time Series (en Inglés)"

This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential "on-line" methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises.Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, "Change Point Analysis for Time Series" offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.