Series:

Wiley Series in Probability and Statistics

3 primary works • 5 total works

Book 285

Cover of Theory of Ridge Regression Estimation with Applications

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications

Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis.

Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource:

  • Offers theoretical coverage and computer-intensive applications of the procedures presented
  • Contains solutions and alternate methods for prediction accuracy and selecting model procedures
  • Presents the first book to focus on ridge regression and unifies past research with current methodology
  • Uses R throughout the text and includes a companion website containing convenient data sets

Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

Book 517

Cover of Theory of Preliminary Test and Stein-Type Estimation with Applications
Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope of the applications. This book contains clear and detailed coverage of basic terminology related to various topics, including: Simple linear model; ANOVA; parallelism model; multiple regression model with non-stochastic and stochastic constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data models Normal, non-normal, and nonparametric theory of estimation Bayes and empirical Bayes methods R-estimation and U-statistics Confidence set estimation

Book 910

Cover of An Introduction to Probability and Statistics

Cover of An Introduction to Probability and Statistics 3e
A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics.
An Introduction to Probability and Statistics, Third Edition includes: * A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression * A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics * Additional topical coverage on bootstrapping, estimation procedures, and resampling * Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals * Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks * Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Cover of Order Statistics and Their Applications