The Canadian Journal of Statistics has recently published a special issue. The Special Issue: 50th anniversary of CJS / 50e anniversaire de la RCS (50:4) includes 15 papers covering a variety of topics that reflect the diversity and excellence of research conducted by Canadian statisticians. This special issue celebrates the 50th anniversary of The Canadian Journal of Statistics (CJS) and the Statistical Society of Canada (SSC). A portion of the introduction by guest editors Bruce Smith, Wendy Lou, Grace Y. Yi ND and Bruno Rémillard is included below:
In “D. A. S. Fraser: From structural inference to asymptotics“, Nancy Reid honours the late Don A.S. Fraser as she describes how Don’s work has influenced asymptotic theory in the statistical sciences.
In “A random walk through Canadian contributions on empirical processes and their applications in probability and statistics“, Csörgő, Dawson, Nasri, and Rémillard, review 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.
In “On the singular gamma, Wishart, and beta matrix-variate density functions“, Mathai and Provost explore the densities of singular matrices constructed from the product of Gaussian matrices, extending the Wishart distribution.
In “Pseudo empirical likelihood inference for nonprobability survey samples“, Chen, Li, Rao, and Wu discusses inference for nonprobability survey samples using pseudo empirical likelihood methods; exploring the contributions of Canadian researchers to these topics.
In “Statistical inference from finite population samples: A critical review of frequentist and Bayesian approaches“, Beaumont and Haziza present a critical review of three estimation approaches for finite population samples: Bayesian, parametric, and nonparametric.
In “Reflections on Bayesian inference and Markov chain Monte Carlo” (OPEN ACCESS), Craiu, Gustafson, and Rosenthal 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.
In “Let’s practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments” (OPEN ACCESS), Chipman and Bingham propose the use of the design and analysis of experiments to improve simulation studies.
In “Robust reflections“, Andrews and Field reflect on the challenges of analyzing increasingly complex data with robust methods.
In “Sparse Estimation of Historical Functional Linear Models with a Nested Group Bridge Approach“, Xun, Guan, and Cao deal with functional data estimation, assuming a short-term dependence and using finite element methods.
In “Life history analysis with multistate models: A review and some current issues“, Cook and Lawless highlight issues of life history analysis with multistate models, including recent advances and future challenges.
In “Causal inference: Critical developments, past and future“, Moodie and Stephens provide an overview of causal inference, historical developments, and current research directions.
In “Unifying genetic association tests via regression: Prospective and retrospective, parametric and nonparametric, and genotype- and allele-based tests“(OPEN ACCESS), Zhang and Sun 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.
In “Complex Statistical Modelling for Phylogenetic Inference“, Susko presents complex models used for phylogenetic inference as well as future research directions in this field.
In “Canadian contributions to environmetrics“(OPEN ACCESS), Dean, El-Shaarawi, Esterby, Mills Flemming, Routledge, Taylor, Woolford, Zidek, and Zwiers review the important and successful Canadian contributions to environmetrics over the last 40 years.
In “The Canadian Statistical Sciences Institute 2003–2022“, the last article, Thompson, Reid, and Estep, describe the progress of the Canadian Statistical Sciences Institute (CANSSI), from the creation of the National Program on Complex Data Structures in 2003 to CANSSI as it is today.
Please note the invited talks related to the articles by Reid, Csörgő et al., Dean et al., and Thompson et al. were presented at the Joint Statistical Meetings 2022 in Washington, DC, by Nancy Reid, Bouchra Nasri, Charmaine Dean, and Don Estep.More Details