Includes bibliographical references and index.

Introduction to machine learning -- Post shrinkage strategies in sparse regression models -- Shrinkage strategies in high-dimensional regression models -- Shrinkage estimation strategies in partially linear models -- Shrinkage strategies : generalized linear models -- Post shrinkage strategy in sparse linear mixed models -- Shrinkage estimation in sparse nonlinear regression models -- Shrinkage strategies in sparse robust regression models -- Liu-type shrinkage estimations in linear sparse models.

"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"-- Provided by publisher.