Name: Direction Dependence Analysis. Foundations and Statistical Methods (en Inglés)
Price: 58.56 USD
Availability: InStock
Author: Alexander von Eye;Wolfgang Wiedermann
ISBN: 9781009381390

portada Direction Dependence Analysis. Foundations and Statistical Methods (en Inglés)

Direction Dependence Analysis. Foundations and Statistical Methods (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Alexander von Eye;Wolfgang Wiedermann

Año

2025

Idioma

Inglés

N° páginas

388

Encuadernación

Tapa Blanda

Dimensiones

15.20 x 23.00 x 2.30 cm

ISBN13

9781009381390

Direction Dependence Analysis. Foundations and Statistical Methods (en Inglés)

Alexander Von Eye;Wolfgang Wiedermann (Autor) · Cambridge University Press · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 22 días háb.

$ 106.48$ 58.56

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 50 unidades

$ 58.56

Llega entre el 03 Sep y el 15 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Direction Dependence Analysis. Foundations and Statistical Methods (en Inglés)"

\n
\nDiscover the groundbreaking Direction Dependence Analysis (DDA), a powerful statistical method that enhances traditional regression and structural modeling by evaluating causal direction between variables. This book offers formal DDA methodologies, real-world applications, and introduces user-friendly DDA software for effective data analysis.
\n
\nWhile regression analysis is widely understood, it falls short in determining the causal direction of relationships in observational data. In this groundbreaking volume, Wiedermann and von Eye introduce Direction Dependence Analysis (DDA), a novel method that leverages variable information often overlooked by traditional techniques, such as higher-order moments like skewness and kurtosis. DDA reveals the asymmetry properties of regression and correlation, enabling researchers to evaluate competing causal hypotheses, assess the roles of variables in causal flows, and develop statistical methods for testing causal direction. This book provides a comprehensive formal description of DDA, illustrated with both artificial and real-world data examples. Additionally, readers will find free software implementations of DDA, making this an essential resource for researchers seeking to enhance their understanding of causal relationships in data analysis.