Advanced Linear Modeling : Statistical Learning and Dependent Data Hardback - 2019
by Ronald Christensen
- New
- Hardback
A$221.85
A$15.57
Delivery to USA
Standard delivery: 7 to 12 days
More delivery options
Standard delivery: 7 to 12 days
Ships from Ria Christie Collections (Greater London, United Kingdom)
Details
- Title Advanced Linear Modeling : Statistical Learning and Dependent Data
- Author Ronald Christensen
- Binding Hardback
- Condition New
- Pages 608
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2019-12-20
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # ria9783030291631_inp
- ISBN 9783030291631 / 3030291634
- Weight 2.33 lbs (1.06 kg)
- Dimensions 9.21 x 6.14 x 1.38 in (23.39 x 15.60 x 3.51 cm)
- Category Mathematics
- Dewey Decimal Code 518
- Quantity available 687
About Ria Christie Collections Greater London, United Kingdom
Biblio member since 2014
Hello We are professional online booksellers. We sell mostly new books and textbooks and we do our best to provide a competitive price. We are based in Greater London, UK. We pride ourselves by providing a good customer service throughout, shipping the items quickly and replying to customer queries promptly. Ria Christie Collections
30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.
Reader reviews for Advanced Linear Modeling : Statistical Learning and Dependent Data
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
From the rear cover
Now in its third edition, this companion volume to Ronald Christensen's Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory--best linear prediction, projections, and Mahalanobis distance-- to extend standard linear modeling into the realms of Statistical Learning and Dependent Data.
This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online.
This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online.
