Canadian Journal of Statistics-Revue Canadienne De Statistique最新文献

Serology tests for SARS-CoV-2 provide a paradigm for estimating the number of individuals who have had an infection in the past (including cases that are not detected by routine testing, which has varied over the course of the pandemic and between jurisdictions). Such estimation is challenging in cases for which we only have limited serological data and do not take into account the uncertainty of the serology test. In this work, we provide a joint Bayesian model to improve the estimation of the sero-prevalence (the proportion of the population with SARS-CoV-2 antibodies) through integrating multiple sources of data, priors on the sensitivity and specificity of the serological test, and an effective epidemiological dynamics model. We apply our model to the Greater Vancouver area, British Columbia, Canada, with data acquired during the pandemic from the end of January to May 2020. Our estimated sero-prevalence is consistent with previous literature but with a tighter credible interval.

Big data present new theoretical and computational challenges as well as tremendous opportunities in many fields. In health care research, we develop a novel divide-and-conquer (DAC) approach to deal with massive and right-censored data under the accelerated failure time model, where the sample size is extraordinarily large and the dimension of predictors is large but smaller than the sample size. Specifically, we construct a penalized loss function by approximating the weighted least squares loss function by combining estimation results without penalization from all subsets. The resulting adaptive LASSO penalized DAC estimator enjoys the oracle property. Simulation studies demonstrate that the proposed DAC procedure performs well and also reduces the computation time with satisfactory performance compared with estimation results using the full data. Our proposed DAC approach is applied to a massive dataset from the Chinese Longitudinal Healthy Longevity Survey.

We consider the problem of testing for treatment effect heterogeneity in observational studies and propose a nonparametric test based on multisample $��U�$-statistics. To account for potential confounders, we use reweighted data where the weights are determined by estimated propensity scores. The proposed method does not require any parametric assumptions on the outcomes and bypasses the need for modelling the treatment effect for each study subgroup. We establish the asymptotic normality for the test statistic and demonstrate its superior numerical performance over several competing approaches via simulation studies. Two real data applications are discussed: an employment programme evaluation study and a mental health study of China's one-child policy.

Statisticians recommend design and analysis of experiments (DAE) for evidence-based research but often use tables to present their own simulation studies. Could DAE do better? We outline how DAE methods can be used to plan and analyze simulation studies. Tools for planning include cause-and-effect diagrams and factorial and fractional factorial designs. Analysis is carried out via analysis of variance, main effect and interaction plots, and other DAE tools. We also demonstrate how Taguchi robust parameter design can be used to study the robustness of methods to a variety of uncontrollable population parameters.

Starting with Tukey's pioneering work in the 1970s, the notion of depth in statistics has been widely extended, especially in the last decade. Such extensions include those to high-dimensional data, functional data, and manifold-valued data. In particular, in the learning paradigm, the depth-depth method has become a useful technique. In this article, we extend the lens depth to the case of data in metric spaces and study its main properties. We also introduce, for Riemannian manifolds, the weighted lens depth. The weighted lens depth is nothing more than a lens depth for a weighted version of the Riemannian distance. To build it, we replace the geodesic distance on the manifold with the Fermat distance, which has the important property of taking into account the density of the data together with the geodesic distance. Next, we illustrate our results with some simulations and also in some interesting real datasets, including pattern recognition in phylogenetic trees, using the depth-depth approach.

In survey sampling, data are obtained on a subset of a finite population by probability or nonprobability sampling procedures. These data are used to compute point estimates of finite population parameters along with their associated variance estimates and confidence intervals. Methods to conduct inferences and evaluate the properties of sampling and estimation procedures have been the subject of discussion and debate in the second half of the 20th century. In this article, we propose a critical review of three inferential approaches in a finite population context: the design-based approach, the frequentist model-based approach, and the Bayesian approach.

Don Fraser was my collaborator and life partner, so I had a uniquely close view of his life in research. This note describes how his early work in the structure of models informed our work in asymptotic theory.

Life history analysis has evolved in the last 50 years as a methodology for analyzing processes associated with human health, education, employment, and other areas. The complexity of many processes, the difficulty of obtaining complete and accurate data, and the increased use of observational data from registries and administrative sources have posed many recent challenges. We review the evolution of life history analysis, discuss some recent work, and consider three areas currently receiving much attention. A theme we stress is the use of expanded models that include selection and observation processes for studies in addition to the life history process of interest. Examples from health research are presented.

Two senior statisticians/data scientists reflect on the challenges arising from the analysis of increasingly complex data using robustness. They include some thoughts on the types of robust analysis that will be needed in the future, while cognizant of our very limited ability to successfully predict the future.