High-Dimensional Regression Modeling
Methodology, Applications, and Software
Chapman & Hall/CRC (Publisher)
1st Edition
Will be published approx. on 15. March 2030
Book
Hardback
450 pages
978-1-4822-4997-2 (ISBN)
Description
This book covers the methodology, applications, and software used in high-dimensional regression modeling. Data collected in many fields is high-dimensional in the sense that many characteristics, or features, are recorded for each observation. The collection of this kind of data is a relatively recent phenomenon, and it poses many challenges that traditional statistical methods have proven incapable of addressing. During the past decade, penalized regression models have become a widespread and important tool for analyzing these kinds of data sets.
More details
Series
Language
English
Place of publication
Boca Raton
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
This book is intended for researchers and graduate students in statistics, biostatistics, and machine learning.
Illustrations
20 s/w Abbildungen
20 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-4822-4997-2 (9781482249972)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Author
University of Iowa, Iowa City, USA
University of Iowa, Iowa City, USA
Content
FOUNDATIONS. Introduction. The Lasso. Bias reduction. Stability and ridge-type penalties. INFERENCE. False discovery rates. Confidence intervals and hypothesis tests. Variable selection with FDR control. Resampling approaches to inference. OTHER LIKELIHOOD/LOSS FUNCTIONS. Logistic regression and generalized linear models. Cox regression. Accelerate failure time model. Robust regression. STRUCTURED SPARSITY. Bi-level selection. Fusion penalties. Additive and semiparametric models. Multivariate outcomes. Variable selection for interactions.