
Автор: Syed Ejaz Ahmed, Feryaal Ahmed, Bahadir Yuzbası
Издательство: Springer
Год: 2023
Страниц: 409
Язык: английский
Формат: pdf (true)
Размер: 18.7 MB
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 term learning in Computer Science is referred to as a branch of Artificial Intelligence (AI) the design and development of algorithms that allows computers to evolve based on empirical data. The type of algorithm is what dictates the success of the Machine Learning (ML) system.