CJS guest editors’ introduction to the special issue in honour of Nancy Reid


We are delighted to present the September 2023 issue of the Canadian Journal of Statistics (CJS), compiled in honour of Professor Nancy Reid.

This issue includes contributed articles by a group of participants who attended a workshop entitled “Statistics at its Best” in Toronto on May 5, 2022. The workshop was organized by the Department of Statistical Sciences at the University of Toronto to celebrate Professor Reid’s 70th birthday. It highlighted her remarkable contributions to statistical science and her dedication to the profession, exemplified in research, leadership, service, and education of the next generation of statisticians. Professor Reid’s impactful career has played a crucial role in fostering the growth of the Canadian statistical community. This workshop was part of a series of celebratory activities coordinated by the Statistical Society of Canada, marking the 50th anniversary of the statistical community in this country.

The special issue encompasses a wide range of topics. Craiu and Yi [1] present an interview article to shed light on Professor Reid’s perspectives on statistical science and data science. Battey [2] discusses the pioneering work on parameter orthogonalization by Cox and Reid as an inducement of abstract population-level sparsity. McCullagh [4] highlights the ambiguity and potential misinterpretation of the standard repeated-sampling concept of the variance in a finite-dimensional parametric model.

Concerning hypothesis testing for parameters on the boundary of their domain, Elkantassi, BellioBrazzale, and Davison [3] discuss various problems, including hard and soft boundaries, and iceberg estimators and propose remedies accordingly. Carey, Genest, and Ramsay [5] tackle the challenging task of estimating a density within Pearson’s system, a class of models encompassing many classical univariate distributions. Urban, Bong, Orellana, and Kass [6] explore multiple oscillating time series in the frequency domain and discuss the complex-valued correlation, its similarities to real-valued Pearson correlation, and dependence among oscillating series using the multivariate complex normal distribution.

Next, McCormack and Hoff [7] consider the problem of low power in standard F-tests for group-specific linear hypotheses in multigroup data with small within-group sample sizes, and they derive alternative test statistics, leveraging information sharing across groups. Pace, Salvan, and Sartori [8] examine confidence sequences in parametric statistical models based on likelihood ratios. They also extend the use of mixture confidence sequences to pseudo-likelihoods, particularly composite likelihood.

Finally, Kalbfleisch and Xu [9] explore rerandomization and optimal matching in observational studies. They address the issue of chance imbalances in treatment groups, which are common in applications, especially in cluster randomized trials with relatively few and highly heterogeneous clusters. 

Enjoy reading!

Grace Y. Yi (University of Western Ontario)
Radu Craiu (University of Toronto)
Guest Coeditors

Table of Contents of the September 2023 Issue of the Canadian Journal of Statistics

[1] Radu V. CRAIU and Grace Y. YI. “A conversation with Nancy Reid”.

[2] Heather S. BATTEY. “Inducement of population sparsity”.

[3] Soumaya ELKANTASSI, Ruggero BELLIO, Alessandra R. BRAZZALE, and Anthony C.
      DAVISON. “Improved inference for a boundary parameter”.

[4] Peter MCCULLAGH. “A tale of two variances”.

[5] Michelle CAREY, Christian GENEST, and James O. RAMSAY. “Sparse estimation within
      Pearson’s system, with an application to financial market risk”.

[6] Konrad N. URBAN, Heejong BONG, Josue ORELLANA, and Robert E. KASS.    
      “Oscillating neural circuits: Phase, amplitude, and the complex normal distribution”.

[7] Andrew MCCORMACK and Peter D. HOFF. “Tests of linear hypotheses using indirect   

[8] Luigi PACE, Alessandra SALVAN, and Nicola SARTORI. “Confidence sequences with
      composite likelihoods”.

[9] John D. KALBFLEISCH and Zhenzhen XU. “Rerandomization and optimal matching”.

Monday, July 24, 2023

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