Journal of Data Science

In 2022 the American Statistical Association established the Riffenburgh Award, which recognizes exceptional innovation in extending statistical methods across diverse fields. Simultaneously, the Department of Statistics at the University of Connecticut proudly commemorated six decades of excellence, having evolved into a preeminent hub for academic, industrial, and governmental statistical grooming. To honor this legacy, a captivating virtual dialogue was conducted with the department’s visionary founder, Dr. Robert H. Riffenburgh, delving into his extraordinary career trajectory, profound insights into the statistical vocation, and heartfelt accounts from the faculty and students he personally nurtured. This multifaceted narrative documents the conversation with more detailed background information on each topic covered by the interview than what is presented in the video recording on YouTube.

In the form of a scholarly exchange with ChatGPT, we cover fundamentals of modeling stochastic dependence with copulas. The conversation is aimed at a broad audience and provides a light introduction to the topic of copula modeling, a field of potential relevance in all areas where more than one random variable appears in the modeling process. Topics covered include the definition, Sklar’s theorem, the invariance principle, pseudo-observations, tail dependence and stochastic representations. The conversation also shows to what degree it can be useful (or not) to learn about such concepts by interacting with the current version of a chatbot.

This paper aims to determine the effects of socioeconomic and healthcare factors on the performance of controlling COVID-19 in both the Southern and Southeastern United States. This analysis will provide government agencies with information to determine what communities need additional COVID-19 assistance, to identify counties that effectively control COVID-19, and to apply effective strategies on a broader scale. The statistical analysis uses data from 328 counties with a population of more than 65,000 from 13 states. We define a new response variable by considering infection and mortality rates to capture how well each county controls COVID-19. We collect 14 factors from the 2019 American Community Survey Single-Year Estimates and obtain county-level infection and mortality rates from USAfacts.org. We use the least absolute shrinkage and selection operator (LASSO) regression to fit a multiple linear regression model and develop an interactive system programmed in R shiny to deliver all results. The interactive system at https://asa-competition-smu.shinyapps.io/COVID19/ provides many options for users to explore our data, models, and results.

We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly.

Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with R package wdnet. The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in C++. Usages of the package are illustrated with detailed explanation. A benchmark study shows that wdnet is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, wdnet provides comparable efficiency.

A/B testing is widely used for comparing two versions of a product and evaluating new proposed product features. It is of great importance for decision-making and has been applied as a golden standard in the IT industry. It is essentially a form of two-sample statistical hypothesis testing. Average treatment effect (ATE) and the corresponding p-value can be obtained under certain assumptions. One key assumption in traditional A/B testing is the stable-unit-treatment-value assumption (SUTVA): there is no interference among different units. It means that the observation on one unit is unaffected by the particular assignment of treatments to the other units. Nonetheless, interference is very common in social network settings where people communicate and spread information to their neighbors. Therefore, the SUTVA assumption is violated. Analysis ignoring this network effect will lead to biased estimation of ATE. Most existing works focus mainly on the design of experiment and data analysis in order to produce estimators with good performance in regards to bias and variance. Little attention has been paid to the calculation of p-value. We work on the calculation of p-value for the ATE estimator in network A/B tests. After a brief review of existing research methods on design of experiment based on graph cluster randomization and different ATE estimation methods, we propose a permutation method for calculating p-value based on permutation test at the cluster level. The effectiveness of the method against that based on individual-level permutation is validated in a simulation study mimicking realistic settings.

Linear regression models are widely used in empirical studies. When serial correlation is present in the residuals, generalized least squares (GLS) estimation is commonly used to improve estimation efficiency. This paper proposes the use of an alternative estimator, the approximate generalized least squares estimators based on high-order AR(p) processes (GLS-AR). We show that GLS-AR estimators are asymptotically efficient as GLS estimators, as both the number of AR lag, p, and the number of observations, n, increase together so that $p=o({n^{1/4}})$ in the limit. The proposed GLS-AR estimators do not require the identification of the residual serial autocorrelation structure and perform more robust in finite samples than the conventional FGLS-based tests. Finally, we illustrate the usefulness of GLS-AR method by applying it to the global warming data from 1850–2012.

Statistical learning methods have been growing in popularity in recent years. Many of these procedures have parameters that must be tuned for models to perform well. Research has been extensive in neural networks, but not for many other learning methods. We looked at the behavior of tuning parameters for support vector machines, gradient boosting machines, and adaboost in both a classification and regression setting. We used grid search to identify ranges of tuning parameters where good models can be found across many different datasets. We then explored different optimization algorithms to select a model across the tuning parameter space. Models selected by the optimization algorithm were compared to the best models obtained through grid search to select well performing algorithms. This information was used to create an R package, EZtune, that automatically tunes support vector machines and boosted trees.

Identifying treatment effect modifiers (i.e., moderators) plays an essential role in improving treatment efficacy when substantial treatment heterogeneity exists. However, studies are often underpowered for detecting treatment effect modifiers, and exploratory analyses that examine one moderator per statistical model often yield spurious interactions. Therefore, in this work, we focus on creating an intuitive and readily implementable framework to facilitate the discovery of treatment effect modifiers and to make treatment recommendations for time-to-event outcomes. To minimize the impact of a misspecified main effect and avoid complex modeling, we construct the framework by matching the treated with the controls and modeling the conditional average treatment effect via regressing the difference in the observed outcomes of a matched pair on the averaged moderators. Inverse-probability-of-censoring weighting is used to handle censored observations. As matching is the foundation of the proposed methods, we explore different matching metrics and recommend the use of Mahalanobis distance when both continuous and categorical moderators are present. After matching, the proposed framework can be flexibly combined with popular variable selection and prediction methods such as linear regression, least absolute shrinkage and selection operator (Lasso), and random forest to create different combinations of potential moderators. The optimal combination is determined by the out-of-bag prediction error and the area under the receiver operating characteristic curve in making correct treatment recommendations. We compare the performance of various combined moderators through extensive simulations and the analysis of real trial data. Our approach can be easily implemented using existing R packages, resulting in a straightforward optimal combined moderator to make treatment recommendations.