Читать книгу Rank-Based Methods for Shrinkage and Selection - A.K. Saleh Md.Ehsanes, A. K. Md. Ehsanes Saleh - Страница 12

It is my pleasure to write this foreword for Professor Saleh’s latest book, “Rank-based Methods for Shrinkage and Selection with Application to Machine Learning”. I have known Professor Saleh for many decades as a leader in Canadian statistics and looked forward to meeting him regularly at the Annual Meeting of the Statistical Society of Canada.

We are well into the golden age of probability and statistics with the emergence of data science and machine learning. Many decades ago, we could attract many bright students to this field of endeavor but today the interest is overwhelming. The connection between theoretical statistics and applied statistics is an important part of machine learning and data science. In order to engage fully in data science, one needs a solid understanding of both the theoretical and the practical aspects of probability and statistics.

The book is unique in presenting a comprehensive approach to inference in regression models based on ranks. It starts with the basics, which enables rapid understanding of the innovative ideas in the rest of the book. In addition to the more familiar aspects of rank-based methods such as comparisons among groups, linear regression, and time series, the authors show how many machine-learning tools can be made more robust via rank-based methods. Modern approaches to model selection, logistic regression, neural networks, elastic net, and penalized regression are studied through this lens. The work is presented clearly and concisely, and highlights many areas for further investigation.

Professor Saleh’s wealth of experience with ridge regression, Stein’s method, and preliminary test estimation informs the arc of the book, and he and his co-authors have built on his expertise to expand the application of rank-based approaches to modern big-data and high-dimensional settings. Careful attention is paid throughout to both theoretical rigor and engaging applications. The applications are made accessible through detailed discussion of computational methods and their implementation in the R and Python computing environments. Its broad coverage of topics, and careful attention to both theory and methods, ensures that this book will be an invaluable resource for students and researchers in statistics.

The authors have identified many areas of useful future research that could be pursued by graduate students and practitioners alike. In this regard, this book is an important contribution in the ongoing research towards robust data science.

Professor N. Reid

University of Toronto

June 2021