International Statistical Review Special Issue: Data Science Versus Classical Inference: Prediction, Estimation, and Attribution

International Statistical Review has just published a special issue “Data Science versus Classical Inference: Prediction, Estimation, and Attribution,” honouring Professor Brad Efron’s International Prize in Statistics in 2019.

The foreword from the special issue by the Co-Editors-in-Chief Nalini Ravishanker and Scott H. Holan is reproduced below.

The 2019 International Prize in Statistics was awarded to Professor Efron at the ISI WSC 2019 in Kuala Lumpur, where he presented an excellent video lecture on “Prediction, Estimation, and Attribution.” This was discussed by Noel Cressie and Anthony Davison. A discussion paper from Prof. Efron’s talk appeared in the June 2020 issue of the Journal of the American Statistical Association (JASA), which included the discussions by Professors Cressie and Davison, along with those from several other leading researchers. We are extremely grateful to Ron Wasserstein (Executive Director, ASA) and Regina Liu (Theory and Methods co‐Editor, JASA) for their generosity in allowing us to share this article with the global readership of the IS Review and to Professors Ray Chambers and Vijay Nair for useful discussions during the formation of this special issue. We are also grateful to Eric Sampson (Journals Manager at the ASA), Steve Raywood (Wiley), Taylor & Francis and the International Statistical Institute (ISI), for making this happen seamlessly.

The lead article in this special issue is an extremely captivating interview of Professor Efron by his Stanford colleague, Professor Balasubramanian Narasimhan. The interview is followed by a republication of the full JASA article, together with all the discussions. Following this, we are very pleased to publish five invited articles by leaders in statistics and data science – Professors David Blei, Peter Bühlmann, Louise Ryan, Rob Tibshirani and Bin Yu – and their respective colleagues. These are in‐depth articles that elaborate on five different topics that fall in the sphere of Professor Efron’s lecture/article.

Professor Efron’s main paper offers several deep insights. The discussions of his paper also indicate several interesting directions for additional research. This is also true of the last five papers. In fact, each article in this special issue provides a review and new research on topics and can generate several off‐shoot discussions and future research papers. We are extremely grateful to all the contributors for making this special issue of the IS Review possible. On behalf of the editorial board of the IS Review, we hope that you enjoy reading this special issue.

Best wishes,

Co‐EICs, International Statistical Review