Name: Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (en Inglés)
Price: 224.68 USD
Availability: InStock
Author: Konishi, Sadanori
ISBN: 9781466567283

portada Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (en Inglés)

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (en Inglés)

Formato

Libro Físico

Autor

Sadanori Konishi

Editorial

CRC Press

Año

2014

Idioma

Inglés

N° páginas

338

Encuadernación

Tapa Dura

Dimensiones

23.6 x 16.3 x 2.3 cm

Peso

0.64 kg.

ISBN13

9781466567283

Categorías

Probabilidad y estadística

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (en Inglés)

Sadanori Konishi (Autor) · CRC Press · Tapa Dura

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Reseña del libro "Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (en Inglés)"

Select the Optimal Model for Interpreting Multivariate DataIntroduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering.The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection.For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.