Free delivery for orders over 69.99 euro.

Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

Free delivery for purchases over 69.99 €

DPD courier 5.99 € Bpost point 7.99 € Bpost 7.49 € DPD point 3.49 € GLS courier 4.49 €

Contact

How to shop

Help

My account

▸ Empty :-(

Free delivery for orders over 69.99 euro.

Robust Partial Least Squares

Name: Robust Partial Least Squares
Brand: VDM Verlag
SKU: 06836476
Price: 51.54 EUR
Availability: InStock
Author: Asuman Turkmen
ISBN: 9783639294019

Regression and Classification

Asuman Turkmen

Nedret Billor

Language

English

Book Paperback

Libristo code: 06836476

Publishers VDM Verlag, October 2010

Partial least squares (PLS) has become an important statistical tool for modeling relations between... Full description

Libristo code: 06836476

125 b

51.54 €

In stock at our supplier Shipping in 9-15 days

30-day return policy

Customers also purchased

Toxicity of pesticides and their mixture to primary producers Stefanie Knauert

Paperback

71.19 €

Grundlagen der Sportentwicklung in Wuppertal Horst Hübner

Paperback

25.61 €

La Radionavigation et l'IFR. L'IFR, le GPS - Tome 2. 2e édition Mougery

Paperback

40.30 €

Partial least squares (PLS) has become an important statistical tool for modeling relations between sets of observed variables by means of latent variables especially for statistical problems dealing with high dimensional data sets. Despite of the fact that PLS handles multicollinearity problem, it fails to deal with data containing outliers since it is based on maximizing the sample covariance matrix between the response(s) and a set of explanatory variables, which is known to be sensitive to outliers. Existence of multicollinearity and outliers is no exception in real data sets, and it leads to a requirement of robust PLS methods in several application areas such as chemometrics. This book provides a detailed literature review on classical and robust PLS methods. The book introduces two new robust PLS methods that can be applied to regression (RoPLS) and classification (RoCPLS) problems. The content of the book should be especially useful for graduate students in statistics as a complementary material for classical multivariate data analysis courses and other researchers who can adopt the provided methodologies for real data analysis.