Introduction to the special issue on the 50th anniversary of CJS

With great pleasure, we present these introductory editorial lines to celebrate the 50th anniversary of The Canadian Journal of Statistics (CJS) and the statistical community of Canada. The 50th anniversary committee for the celebrations, with the support of former editor-in-chief Fang Yao, decided to run a special issue to mark the occasion. We were fortunate to receive 15 interesting manuscripts covering a broad variety of research topics and reflecting the diversity and excellence of the research conducted by Canadian statisticians. Before describing those contributions, we would like to thank the former and current editors-in-chief of CJS, Fang Yao and Johanna G. Nešlehová, for their work setting up this special issue, with help from their assistant Julie Falkner. We are also grateful to the managing editor, Bouchra Nasri, who assisted us from the beginning and arranged with Wiley a year of free-to-read access to the articles of this issue. The opening article is contributed by Nancy Reid in honour of the late Don A.S. Fraser. She elegantly describes how Don’s work has influenced asymptotic theory in the statistical sciences. The article recalls Don’s great memories and good humour around the philosophical trends of estimation theory. Two mathematical statistics articles follow. The first of these, by Csörgő, Dawson, Nasri, and Rémillard, reviews the contributions of some Canadian statisticians to empirical processes, including copula processes, with applications to goodness-of-fit tests, change-point tests, and tests of independence, among others. The second, by Mathai and Provost, explores the densities of singular matrices constructed from the product of Gaussian matrices, extending the Wishart distribution. Our focus then moves to sampling theory. The article by Chen, Li, Rao, and Wu discusses inference for nonprobability survey samples using pseudo empirical likelihood methods; it explores the contributions of Canadian researchers to these topics. Beaumont and Haziza then present a critical review of three estimation approaches for finite population samples: Bayesian, parametric, and nonparametric. The next topic is computational statistics and complex data analysis. First, Andrews and Field reflect on the challenges of analyzing increasingly complex data with robust methods. Craiu, Gustafson, and Rosenthal then give an overview of recent advances in Bayesian inference and Markov chain Monte Carlo methods, highlighting the challenges posed by big data and intractable likelihoods. The third article, by Chipman and Bingham, proposes the use of the design and analysis of experiments to improve simulation studies. Finally, Xun, Guan, and Cao deal with functional data estimation, assuming a short-term dependence and using finite element methods. The section on topics in biostatistics begins with an interesting review by Cook and Lawless that highlights issues of life history analysis with multistate models, including recent advances and future challenges. Moodie and Stephens provide an overview of causal inference, historical developments, and current research directions. Zhang and Sun then explore the use of regression methods for a unified approach to genetic association tests, showing that developing robust methods for association is still a domain of interest. Finally, Susko presents complex models used for phylogenetic inference as well as future research directions in this field. Our final articles discuss collaborative work. Dean, El-Shaarawi, Esterby, Mills Flemming, Routledge, Taylor, Woolford, Zidek, and Zwiers review the important and successful Canadian