The New England Journal of Statistics in Data Science

Anomaly detection plays an important role in traffic operations and control. Missingness in spatial-temporal datasets prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of large amounts of data. This paper proposes an anomaly detection scheme for the 2021 Algorithms for Threat Detection (ATD) challenge based on Gaussian process models that generate features used in a logistic regression model which leads to high prediction accuracy for sparse traffic flow data with a large proportion of missingness. The dataset is provided by the National Science Foundation (NSF) in conjunction with the National Geospatial-Intelligence Agency (NGA), and it consists of thousands of labeled traffic flow records for 400 sensors from 2011 to 2020. Each sensor is purposely downsampled by NSF and NGA in order to simulate missing completely at random, and the missing rates are 99%, 98%, 95%, and 90%. Hence, it is challenging to detect anomalies from the sparse traffic flow data. The proposed scheme makes use of traffic patterns at different times of day and on different days of week to recover the complete data. The proposed anomaly detection scheme is computationally efficient by allowing parallel computation on different sensors. The proposed method is one of the two top performing algorithms in the 2021 ATD challenge.

Discussion of “Four types of frequentism and their interplay with Bayesianism” by Jim Berger.

Platform trials are multiarm clinical studies that allow the addition of new experimental arms after the activation of the trial. Statistical issues concerning “adding new arms”, however, have not been thoroughly discussed. This work was motivated by a “two-period” pediatric osteosarcoma study, starting with two experimental arms and one control arm and later adding two more pre-planned experimental arms. The common control arm will be shared among experimental arms across the trial. In this paper, we provide a principled approach, including how to modify the critical boundaries to control the family-wise error rate as new arms are added, how to re-estimate the sample sizes and provide the optimal control-to-experimental arms allocation ratio, in terms of minimizing the total sample size to achieve a desirable marginal power level. We examined the influence of the timing of adding new arms on the design’s operating characteristics, which provides a practical guide for deciding the timing. Other various numerical evaluations have also been conducted. A method for controlling the pair-wise error rate (PWER) has also been developed. We have published an R package, PlatformDesign, on CRAN for practitioners to easily implement this platform trial approach. A detailed step-by-step tutorial is provided in Appendix A.2.

Bayesian model averaging (BMA) provides a coherent way to account for model uncertainty in statistical inference tasks. BMA requires specification of model space priors and parameter space priors. In this article we focus on comparing different model space priors in the presence of model uncertainty. We consider eight reference model space priors used in the literature and three adaptive parameter priors recommended by Porwal and Raftery [37]. We assess the performance of these combinations of prior specifications for variable selection in linear regression models for the statistical tasks of parameter estimation, interval estimation, inference, point and interval prediction. We carry out an extensive simulation study based on 14 real datasets representing a range of situations encountered in practice. We found that beta-binomial model space priors specified in terms of the prior probability of model size performed best on average across various statistical tasks and datasets, outperforming priors that were uniform across models. Recently proposed complexity priors performed relatively poorly.

We highlight points of agreement between Meng’s suggested principles and those proposed in our 2019 editorial in The American Statistician. We also discuss some questions that arise in the application of Meng’s principles in practice.

Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables.