On the Edge: Statistics & Computing: Reproducible Statistical Research

TL;DR: A number of issues common to both statistical research and collaboration that impact the verification, understanding, and subsequent application of novel statistical procedures are discussed.

Abstract: i\\ lany recent results in statistical research are based on simulation or experiment-based procedures which have been facilitated by technological advances in computing (Beran 200 I), While mathematical theory is still very important, these computational techniques, including Monte-Carlo, i\\ larkov Chain Monte-Carlo, and rcsumpling methods, arc increasingly used to obtain results which sometimes are more relevant than those based upon low-order approximations to asymptotic theory, These simulation-based techniques can help to lill gaps in understanding theoretical and mathematical procedures as well as provide numerical approximations to computationally infeasible exact solutions, This article will discuss a number of issues common to both statistical research and collaboration that impact the verification, understanding, and subsequent application of novel statistical procedures, Complicated numerical algorithms must often he used even when we have sound theoretical results, Implementation of these procedures can be just as difficult as the construction of proofs, However, while publication of research papers is based on the verification or proper referencing of proofs for every theorem, there is a tendency to accept seemingly realistic computational results, as presented by figures and tables, without any proofof correctness. Yl't,these results an,' critical for justifying the proposed methods and represent