BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Applied regularization methods for the social sciences

Applied regularization methods for the social sciences

Applied regularization methods for the social sciences
Stock photo: cover may vary

Applied regularization methods for the social sciences Papeback -

by Holmes Finch

Add to wish list
  • New
  • first
New

Description

pages cm First edition Includes bibliographical references and index. Papeback. New.
Ask the seller a question Add to wish list
A$126.82
A$5.74 Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Ships from Cold Books (New York, United States)
The BIBLIO Guarantee

Details

  • Title Applied regularization methods for the social sciences
  • Author Holmes Finch
  • Binding Papeback
  • Condition New
  • Pages 297
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date pages cm First edition Includ
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 6397673577
  • ISBN 9781032209470 / 103220947X
  • Weight 0.96 lbs (0.44 kg)
  • Dimensions 9.21 x 6.14 x 0.65 in (23.39 x 15.60 x 1.65 cm)
  • Category Sociology
  • Library of Congress subjects Mathematical statistics, Social sciences - Statistical methods
  • Library of Congress Catalogue Number 2021048064
  • Dewey Decimal Code 300.15
  • Quantity available 4

About Cold Books New York, United States

Biblio member since 2012

Terms of Sale: 30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Cold Books

Reader reviews for Applied regularization methods for the social sciences

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

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

Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action.

Key Features:

  • Description of regularization methods in a user friendly and easy to read manner
  • Inclusion of regularization-based approaches for a variety of statistical analyses commonly used in the social sciences, including both univariate and multivariate models
  • Fully developed extended examples using multiple software packages, including R, SAS, and SPSS
  • Website containing all datasets and software scripts used in the examples
  • Inclusion of both frequentist and Bayesian regularization approaches
  • Application exercises for each chapter that instructors could use in class, and independent researchers could use to practice what they have learned from the book

About the author

Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at BSU, and a professor of statistics and psychometrics. His research interests include structural equation modeling, item response theory, educational and psychological measurement, multilevel modeling, machine learning, and robust multivariate inference. In addition to conducting research in the field of statistics, he also regularly collaborates with colleagues in fields such as educational psychology, neuropsychology, and exercise physiology.

Please wait a moment while we load the requested page.

tracking-