Preface for the Special Issue on Distribution Theory, Estimation, and Inference

Distribution theory, estimation, and inference may be considered as the three main cornerstones or building blocks of statistics. Others would say they rather intertwine themselves to make the essence of statistics. Indeed, in one way or another they are ubiquitous in any research in statistics, either theoretical or applied. They have always been the fundamental bases of all aspects associated with statistical studies. In many inferential processes, new and important results on the exact, asymptotic, or near-exact distributions of the associated test statistics and parameter estimators have been applied with considerable success in multivariate analysis; linear, nonlinear, and mixed models; order statistics; and extreme value theory. Also, recent results in estimation, combined with other new results in distribution theory and inference, have made major contributions to a broad spectrum of statistical techniques. This special issue is, as such, dedicated to the public exposure of new results in distribution theory, estimation, and inference. Among the 12 articles in this special issue, the reader may benefit from articles in all the main subareas of distribution theory, estimation, and inference, starting with the first three articles, which bridge from hypotheses testing to distribution theory, passing to the next four articles with a stronger emphasis on aspects directly related with distribution theory, going then through the next three articles on different estimation problems, and finalizing with the last, but by no means less important, two articles on extreme value theory. The guest editors thank all the authors who kindly contributed to this special issue with their articles, their work, and their results, not forgetting all those who acted as reviewers.