Journal of Data Science

High or ultra-high-dimensional data are becoming increasingly common in various fields. They often display diverse characteristics, including heterogeneity, longitudinal responses, and imbalanced measurements. These complexities make it challenging to integrate different modeling options and their combinations in order to fully leverage this rich data source. This paper provides an easy-to-use, and stand-alone, R package, geeVerse, that can implement any combination of 1) simultaneous variable selection and estimation, 2) quantile regression or mean regression for heterogeneous data, 3) longitudinal or cross-sectional data analysis, 4) balanced or imbalanced data, and 5) moderate, high, or even ultra-high-dimensional data. To accomplish this, we propose computationally efficient implementations of penalized generalized estimating equations (GEE) for quantile and mean regression. We present multiple applications with ultra-high-dimensional data including analysis of a resampled genetic dataset, quantile and mean regressions, analysis of cross-sectional and longitudinal data, differing correlation structures, and differing number of repeated measurements per subject. We also demonstrate our approach on two real data applications.

Mediation analysis plays an important role in many research fields, yet it is very challenging to perform estimation and hypothesis testing for high-dimensional mediation effects. We develop a user-friendly $\mathsf{R}$ package HIMA for high-dimensional mediation analysis with varying mediator and outcome specifications. The HIMA package is a comprehensive tool that accommodates various types of high-dimensional mediation models. This paper offers an overview of the functions within HIMA and demonstrates the practical utility of HIMA through simulated datasets. The HIMA package is publicly available from the Comprehensive $\mathsf{R}$ Archive Network at https://CRAN.R-project.org/package=HIMA.

Connections between subpar dietary choices and negative health consequences are well established in the field of nutritional epidemiology. Consequently, in the United States, there is a standard practice of conducting regular surveys to evaluate dietary habits. One notable example is the National Health and Nutrition Examination Survey (NHANES) conducted every two years by the Center for Disease Control (CDC). Several scoring methods have been developed to assess the quality of diet in the overall population as well as different pertinent subgroups using dietary recall data collected in these surveys. The Healthy Eating Index (HEI) is one such metric, developed based on recommendations from the United States Department of Health and Human Services (HHS) and Department of Agriculture (USDA) and widely used by nutritionists. Presently, there is a scarcity of user-friendly statistical software tools implementing the scoring of these standard scoring metrics. Herein, we develop an R package heiscore to address this need. Our carefully designed package, with its many user-friendly features, increases the accessibility of the HEI scoring using three different methods outlined by the National Cancer Institute (NCI). Additionally, we provide functions to visualize multidimensional diet quality data via various graphing techniques, including bar charts and radar charts. Its utility is illustrated with many examples, including comparisons between different demographic groups.

This paper introduces a label-efficient response modelling method useful when the target labels are unknown a priori. Unlike most response modelling methods that adopt a supervised or semi-supervised approach, we apply clustering to partition data into homogeneous segments, which are assumed to reflect the underlying response behaviours. We then take a random sample from each cluster. For each sampled record, the true target label is acquired. Through this cluster-based stratified sampling approach, we reduced the cost of label acquisition needed to estimate the cluster-specific and overall basic response rates. The goal is to identify a subset of the population more likely to respond (e.g., make a purchase) while controlling campaign costs. This idea of subsetting the population represents a departure from conventional classification tasks, which require full labeling of all observations. We regard clusters with response rates significantly higher than the estimated basic response rate as high-propensity clusters and proceed to acquire all their remaining labels. Our experimental results show that the response rates of high-propensity clusters are at least 1.7 times the basic response rate. This suggests that the proposed approach significantly reduces costs by targeting only high-propensity groups and is useful in scenarios lacking historical ground truth.

Stunted growth in children is a worldwide issue which may cause long term problems for individuals stunted as early as two years of age. However, predicting stunted growth with accuracy is quite complex, but machine learning poses a distinct advantage in this regard. While several techniques are available for predictive modeling, the Super Learner stands out as an ensemble method that integrates multiple algorithms into a single predictive model with enhanced performance. In this study, the Super Learner model, comprising generalized linear model, bagged trees, random forests, conditional random forest, stochastic gradient boosting, Bayesian additive regression trees, neural networks, and model averaged neural networks, achieved high performance with high area under the receiver operating characteristic curve, Brier Score, and the minimum of precision and recall values. However, after analyzing the results from cross validation, the final model selected was the Bayesian additive regression trees. Within the final model, the height-for-age z-score at one year, income, expenditure, anti-lipopolysaccharide antibody at week 6 and at week 18, plasma retinol binding protein at week 6, plasma soluble cluster designation 14 at week 18, fecal Reg 1B at week 12, vitamin D at week 18, mother’s weight and height at enrollment, fecal calprotectin at week 12, fecal myeloperoxidase at week 12, number of days of diarrhea through the first year of life, and the number of days of exclusive breastfeeding through the first year of life emerged as the top important variables for predicting stunted growth at two years of age.

Financial news headlines serve as a rich source of information on financial activities, offering a wealth of text that can provide insights into human behavior. One key analysis that can be conducted on this text is sentiment analysis. Despite extensive research over the years, sentiment analysis still faces challenges, particularly in handling internet slang, abbreviations, and emoticons commonly found on many websites that cover financial news headlines, including Bloomberg, Yahoo Finance, and Financial Times. This paper compares the performance of two sentiment analyzers—VADER and TextBlob—on financial news headlines from two countries: the USA (a well-developed economic nation) and Nepal (an underdeveloped economic nation). The collected headlines were manually classified into three categories (positive, negative, and neutral) from a financial perspective. The headlines were then cleaned and processed through the sentiment analyzers to compare their performance. The models’ performance is evaluated based on accuracy, sensitivity, specificity, and neutral specificity. Experimental results reveal that VADER performs better than TextBlob on both datasets. Additionally, both models perform better on financial news headlines from the USA than Nepal. These findings are further validated through statistical tests.

We propose a Bayesian Negative Binomial-Bernoulli model to jointly analyze the patterns behind field goal attempts and the factors influencing shot success. We apply nonnegative CANDECOMP/PARAFAC tensor decomposition to study shot patterns and use logistic regression to predict successful shots. To maintain the conditional conjugacy of the model, we employ a double Pólya-Gamma data augmentation scheme and devise an efficient variational inference algorithm for estimation. The model is applied to shot chart data from the National Basketball Association, focusing on the regular seasons from 2015–16 to 2022–23. We consistently identify three latent features in shot patterns across all seasons and verify a popular claim from recent years about the increasing importance of three-point shots. Additionally, we find that the home court advantage in field goal accuracy disappears in the 2020–21 regular season, which was the only full season under strict COVID-19 crowd control, aside from the short bubble period in 2019–20. This finding contributes to the literature on the influence of crowd effects on home advantage in basketball games.

Detecting illicit transactions in Anti-Money Laundering (AML) systems remains a significant challenge due to class imbalances and the complexity of financial networks. This study introduces the Multiple Aggregations for Graph Isomorphism Network with Custom Edges (MAGIC) convolution, an enhancement of the Graph Isomorphism Network (GIN) designed to improve the detection of illicit transactions in AML systems. MAGIC integrates edge convolution (GINE Conv) and multiple learnable aggregations, allowing for varied embedding sizes and increased generalization capabilities. Experiments were conducted using synthetic datasets, which simulate real-world transactions, following the experimental setup of previous studies to ensure comparability. MAGIC, when combined with XGBoost as a link predictor, outperformed existing models in 16 out of 24 metrics, with notable improvements in F1 scores and precision. In the most imbalanced dataset, MAGIC achieved an F1 score of 82.6% and a precision of 90.4% for the illicit class. While MAGIC demonstrated high precision, its recall was lower or comparable to the other models, indicating potential areas for future enhancement. Overall, MAGIC presents a robust approach to AML detection, particularly in scenarios where precision and overall quality are critical. Future research should focus on optimizing the model’s recall, potentially by incorporating additional regularization techniques or advanced sampling methods. Additionally, exploring the integration of foundation models like GraphAny could further enhance the model’s applicability in diverse AML environments.

Approximately 15% of adults in the United States (U.S.) are afflicted with chronic kidney disease (CKD). For CKD patients, the progressive decline of kidney function is intricately related to hospitalizations due to cardiovascular disease and eventual “terminal” events, such as kidney failure and mortality. To unravel the mechanisms underlying the disease dynamics of these interdependent processes, including identifying influential risk factors, as well as tailoring decision-making to individual patient needs, we develop a novel Bayesian multivariate joint model for the intercorrelated outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and competing-risk terminal events of kidney failure and death. The proposed joint modeling approach not only facilitates the exploration of risk factors associated with each outcome, but also allows dynamic updates of cumulative incidence probabilities for each competing risk for future subjects based on their basic characteristics and a combined history of longitudinal measurements and recurrent events. We propose efficient and flexible estimation and prediction procedures within a Bayesian framework employing Markov Chain Monte Carlo methods. The predictive performance of our model is assessed through dynamic area under the receiver operating characteristic curves and the expected Brier score. We demonstrate the efficacy of the proposed methodology through extensive simulations. Proposed methodology is applied to data from the Chronic Renal Insufficiency Cohort study established by the National Institute of Diabetes and Digestive and Kidney Diseases to address the rising epidemic of CKD in the U.S.

The ultrasonic testing has been considered a promising method for diagnosing and characterizing masonry walls. As ultrasonic waves tend to travel faster in denser materials, their use is common in evaluating the conditions of various materials. Presence of internal voids, e.g., would alter the wave path, and this distinct behavior could be employed to identify unknown conditions within the material, allowing for the assessment of its condition. Therefore, we applied mixed models and Gaussian processes to analyze the behavior of ultrasonic waves on masonry walls and identify relevant factors impacting their propagation. We observed that the average propagation time behavior differs depending on the material for both models. Additionally, the condition of the wall influences the propagation time. Gaussian process and mixed model performances are compared, and we conclude that these models can be useful in a classification model to automatically identify anomalies within masonry walls.