Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data
Syed Ejaz Ahmed author Feryaal Ahmed author Bahadir Yüzbaşı author
Format:Hardback
Publisher:Taylor & Francis Ltd
Published:25th May '23
Should be back in stock very soon
This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in data science. It combines statistical learning and machine learning techniques in a unique and optimal way. It is well-known that machine learning methods are subject to many issues relating to bias, and consequently the mean squared error and prediction error may explode. For this reason, we suggest shrinkage strategies to control the bias by combining a submodel selected by a penalized method with a model with many features. Further, the suggested shrinkage methodology can be successfully implemented for high dimensional data analysis. Many researchers in statistics and medical sciences work with big data. They need to analyse this data through statistical modelling. Estimating the model parameters accurately is an important part of the data analysis. This book may be a repository for developing improve estimation strategies for statisticians. This book will help researchers and practitioners for their teaching and advanced research, and is an excellent textbook for advanced undergraduate and graduate courses involving shrinkage, statistical, and machine learning.
- The book succinctly reveals the bias inherited in machine learning method and successfully provides tools, tricks and tips to deal with the bias issue.
- Expertly sheds light on the fundamental reasoning for model selection and post estimation using shrinkage and related strategies.
- This presentation is fundamental, because shrinkage and other methods appropriate for model selection and estimation problems and there is a growing interest in this area to fill the gap between competitive strategies.
- Application of these strategies to real life data set from many walks of life.
- Analytical results are fully corroborated by numerical work and numerous worked examples are included in each chapter with numerous graphs for data visualization.
- The presentation and style of the book clearly makes it accessible to a broad audience. It offers rich, concise expositions of each strategy and clearly describes how to use each estimation strategy for the problem at hand.
- This book emphasizes that statistics/statisticians can play a dominant role in solving Big Data problems, and will put them on the precipice of scientific discovery.
- The book contributes novel methodologies for HDDA and will open a door for continued research in this...
"Recently, focus has been on estimation techniques after a model selection via shrinkage approaches. The literature in this field has witnessed a huge growth which created a dire need to collect these developments in a single source. This book is a timely answer to such need.
The book is strongly recommended for statistical researchers as well as practitioners intending to advance or apply penalty and shrinkage estimation methodologies."
Abdulkadir Hussein, University of Windsor, Canada, Technometrics, November 2023.
"This book in some ways may be little ahead of its time but provides a potential approach to modelling, via regression, in a high dimensional setting while utilizing the idea of shrinkage. The basic premise is that no piece of information should be thrown away, yet, information can be weighted by its importance. The authors have considered the high dimensional setting with linear, generalized and nonlinear models."
Ravindra Khattree, Oakland University, USA, International Statistical Review, June 2024.
"The book ‘Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data’ fills a critical gap, examining shrinkage strategy against penalty, full model, and submodel estimators. It offers foundational insights into model selection and estimation challenges, uniting theory with practicality. A valuable resource for statisticians and practitioners navigating complex big data scenarios."
Shuangzhe Liu, University of Canberra, Australia, Journal of Applied Statistics, November 2023.
ISBN: 9780367763442
Dimensions: unknown
Weight: 1970g
378 pages