Journal of Data Science

Traditionally z-scores specified from the WHO population growth curves have been used to describe a child’s growth in relation to his age- and sex-matched population distribution. We propose a new regression approach that offers a straightforward interpretation of the relative growth in terms of the original anthropometric variable. We create a hybrid data set consisting of the observations from the study of interest and counterpart pseudo-population observations imputed from the WHO population growth curves matched to each study participant. We then fit linear and quantile regression models to the hybrid data incorporating demographic variables (usually age and biological sex) corresponding to the growth curves of demographically-similar individuals, a study versus population indicator, and its interactions with demographic variables. We further control for confounding variables from the study by adding their interactions with the study indicator variable. The interaction terms between the study indicator and the demographic variables age and biologic sex can be interpreted as relative growth parameters that depict the differences in means (or quantiles) between the study participants and their pseudo-population counterparts of the original anthropometric variables, rather than the associated z-scores. We use anthropometric growth data from a prospective birth cohort study conducted in Uganda for illustration.

The self-organizing map (SOM) is an unsupervised, competitive learning neural network that projects high-dimensional data onto a low-dimensional grid, effectively showcasing the topological relationships within the original dataset. However, the conventional SOM training algorithm is restricted to numeric data. Categorical data typically needs to be converted into binary format before SOM training, which can lead to the loss of crucial similarity information between categorical values. As a result, the trained SOM may not accurately reflect the true topological order. While a training data splitting method (TDSM) can help identify perfect representative neurons and enhance clustering outcomes, the training data itself often lacks sufficient information, such as data distribution, and can be uncertain and ambiguous. Even when perfect neurons are identified, further improvements in clustering results become challenging. This paper investigates the possibility of improving the performance of supervised TDSM SOM clustering by utilizing unsupervised self-organization granule encoding for discrete data. This approach to unsupervised learning is advantageous for uncovering uncertain and ambiguous information within discrete data, leading to a more effective topological representation of the training data.

Joint models can describe the relationship between recurrent and terminal events. Typically, recurrent events are modeled using the total time scale, assuming constant covariate effects on each recurrent event. However, modeling the gap time between recurrent events could allow varying covariate effects and offer greater flexibility and accuracy. For instance, in HIV-infected patients, the intervals between the first occurrence of opportunistic infections (OIs) may follow a different distribution compared to later OIs. However, limited research has focused on mediation analysis using joint modeling of gap times and survival time. In this work, we propose a novel joint modeling approach that studies the mediation effect of recurrent events on survival outcomes by modeling the recurrent events by gap time. This allows us to handle cases where the first occurrence of a recurrent event behaves differently from subsequent events. Additionally, we use a relaxed “sequential ignorability” assumption to address unmeasured confounding. Simulation studies demonstrate that our model performs well in estimating both model parameters and mediation effects. We apply our method to an AIDS study to evaluate the comparative effectiveness of two treatments and the effect of baseline CD4 counts on overall survival, mediated by recurrent opportunistic infections modeled through gap times.

Forecasting business cycles and macroeconomic trends is inherently challenging due to their complex and non-linear relationships with volatile and noisy economic factors. However, the growing availability of large-scale economic data, coupled with advances in computational power, creates new opportunities to extract meaningful information and develop robust predictive models. Deep learning methods are well suited for handling noisy and complex data; nevertheless, their application to business cycle prediction remains at an early stage. To address this gap, this study develops an end-to-end computational framework that implements state-of-art deep learning architectures for identifying business cycle phases. The key contributions of this work include systematic input feature selection across a broad range of economic sectors, advanced preprocessing techniques for noisy data, the development of a customizable and reproducible computational framework, a data-driven approach to hyperparameter tuning, and the use of robust model selection strategies. Twenty two models based on two architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were implemented and demonstrated strong predictive performance, highlighting the effectiveness of the proposed approach for forecasting macroeconomic trends. Among these, the GRU model with 512 neurons achieved the best results, with an accuracy of 86.42%, precision of 92.03%, recall of 93.32%, and an F1-score of 92.34%. Overall, the findings provide valuable insights that can support informed decision-making by policymakers and other stakeholders.

In data analysis, unexpected results often prompt researchers to revisit their procedures to identify potential issues. While some researchers may struggle to identify the root causes, experienced researchers can often quickly diagnose problems by checking a few key assumptions. These checked assumptions, or expectations, are typically informal, difficult to trace, and rarely discussed in publications. In this paper, we introduce the term analysis validation checks to formalize and externalize these informal assumptions. We then introduce a procedure to identify a subset of checks that best predict the occurrence of unexpected outcomes, based on simulations of the original data. The checks are evaluated in terms of accuracy, determined by binary classification metrics, and independence, which measures the shared information among checks. We demonstrate this approach with a toy example using step count data and a generalized linear model example examining the effect of particulate matter air pollution on daily mortality.

The receiver operating characteristics (ROC) curve has been widely used to evaluate the discrimination performance of biomarkers, but it has been criticized for overlooking their underlying distributions. In this paper, we propose a continuous version of the ROC curve that can assess not only the discrimination performance of biomarkers but also their continuity performance. Our method summarizes the biomarker values as conditional tail expectations at varying thresholds and compare them with true positive and false positive rates. The proposed method is particularly useful for an early phase of biomarker study that enrolls heterogeneous disease populations. Analysis of data from an ovarian cancer biomarker study illustrates the practical utility of the proposed method over the standard ROC curve analysis. The proposed methods are implemented in the R package varoc.

Spatial data display correlation between observations collected at nearby locations. Generally, machine and deep learning methods either do not account for this correlation or do so indirectly through correlated features. To account for spatial correlation, we propose preprocessing the data using a spatial decorrelation transform motivated from properties of a multivariate Gaussian distribution and Vecchia approximations. The preprocessed, transformed data can then be ported into a machine or deep learning tool. After model fitting on the transformed data, the output can be spatially re-correlated via the corresponding inverse transformation. We show that including this spatial adjustment results in higher predictive accuracy on simulated and real spatial datasets.

Competitor rating systems for head-to-head games are typically used to measure playing strength from game outcomes. Ratings computed from these systems are often used to select top competitors for elite events, for pairing players of similar strength in online gaming, and for players to track their own strength over time. Most implemented rating systems assume only win/loss outcomes, and treat occurrences of ties as the equivalent to half a win and half a loss. However, in games such as chess, the probability of a tie (draw) is demonstrably higher for stronger players than for weaker players, so that rating systems ignoring this aspect of game results may produce strength estimates that are unreliable. We develop a new rating system for head-to-head games based on a model that explicitly acknowledges that a tie may depend on the strengths of the competitors. The approach uses a Bayesian dynamic modeling framework. Within each time period, posterior updates are computed in closed form using a single Newton-Raphson iteration evaluated at the prior mean. The approach is demonstrated on a large dataset of chess games played in International Correspondence Chess Federation tournaments.

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.

When collaborating with students, colleagues and practitioners, one soon realizes the lack of efficiency when sending around emails with multiple attachments, especially if changes are made in several types of documents (for example, text, code, PDF) and simultaneously by several collaborators. Using a version control system (VCS) can largely improve joint workflows, from file sharing, including merging changes from different collaborators, to providing access to past versions of the shared work, while allowing each collaborator to work under her/his preferred setup (for example, text editor or file manager). There exists lots of technical or specialized information and literature about VCSes online, but, as often, this is rather overwhelming for beginners. Knowing the basics well is more important than getting lost in the vast amount of possible options VCSes offer. Also, the basics are sufficient to enjoy using VCSes and to see their value in collaborative work, additional features can still be picked up along the way once necessary. We focus on such fundamentals of the centralized VCS SVN and the distributed VCS Git. We explain in simple terms how these systems can be set up and interacted with to increase efficiency in collaborative workflows.

Precision medicine is an innovative approach that aims to customize medical treatments and interventions to patients based on their individual characteristics. Several estimation techniques, including Q-learning, have been developed to determine optimal treatment rules. However, the applicability of these methods depends on the availability of precisely measured variables. This study extends the scope of Q-learning to incorporate compound outcomes, deviating from the commonly assumed univariate outcomes, and further accommodates data with mismeasurement in both binary and continuous covariates. Two methods are described to mitigate the impact of mismeasurement. Numerical studies reveal that mismeasurement in covariates leads to notable estimation bias in parameters indexing the optimal treatment, yet the methods addressing the mismeasured effects yield improved results.

Time-to-event data analysis without a well-defined time origin commonly occurs in observational studies that retrospectively collect survival endpoints. For instance, after enrolling participants who have or have not received a specific treatment, an event status can be observed for all participants; however, the start date of treatment is only observable for the treatment group. The corresponding time origin does not exist for the control group, resulting in missing survival time data. Complete-case analysis is often considered the standard approach, but it disregards information from all participants in the control group and does not allow us to compare their survival distributions. To address this challenge, we propose a novel semiparametric proportional hazards model by regarding these missing time origins as nuisance parameters. We approximate the risk sets as cumulative normal distributions to deal with these nuisance parameters and develop estimation and inference procedures for our proposed estimator. We study the asymptotic properties of this model and conduct the simulation studies to validate its finite sample property. Analysis of data from a recent SARS-CoV-2 seroprevaluence study illustrates the applicability of our methods. The proposed methods are implemented in the R package coxphm.

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.

Modern precision medicine aims to utilize real-world data to provide the best treatment for an individual patient. An individualized treatment rule (ITR) maps each patient’s characteristics to a recommended treatment scheme that maximizes the expected outcome of the patient. A challenge precision medicine faces is population heterogeneity, as studies on treatment effects are often conducted on source populations that differ from the populations of interest in terms of the distribution of patient characteristics. Our research goal is to explore a transfer learning algorithm that aims to address the population heterogeneity problem and obtain targeted, optimal, and interpretable ITRs. The algorithm incorporates a calibrated augmented inverse probability weighting estimator for the average treatment effect and employs value function maximization for the target population using Genetic Algorithm to produce our desired ITR. To demonstrate its practical utility, we apply this transfer learning algorithm to two large medical databases, eICU Collaborative Research Database and Medical Information Mart for Intensive Care III. We first identify the important covariates, treatment options, and outcomes of interest based on the two databases, and then estimate the optimal linear ITRs for patients with sepsis. Our research introduces and applies new techniques for data fusion to obtain data-driven ITRs that cater to patients’ individual medical needs in a population of interest. By emphasizing generalizability and personalized decision-making, this methodology extends its potential application beyond medicine to fields such as marketing, technology, social sciences, and education.

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.

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.

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.

Heart rate data collected from wearable devices – one type of time series data – could provide insights into activities, stress levels, and health. Yet, consecutive missing segments (i.e., gaps) that commonly occur due to improper device placement or device malfunction could distort the temporal patterns inherent in the data and undermine the validity of downstream analyses. This study proposes an innovative iterative procedure to fill gaps in time series data that capitalizes on the denoising capability of Singular Spectrum Analysis (SSA) and eliminates SSA’s requirement of pre-specifying the window length and number of groups. The results of simulations demonstrate that the performance of SSA-based gap-filling methods depends on the choice of window length, number of groups, and the percentage of missing values. In contrast, the proposed method consistently achieves the lowest rates of reconstruction error and gap-filling error across a variety of combinations of the factors manipulated in the simulations. The simulation findings also highlight that the commonly recommended long window length – half of the time series length – may not apply to time series with varying frequencies such as heart rate data. The initialization step of the proposed method that involves a large window length and the first four singular values in the iterative singular value decomposition process not only avoids convergence issues but also facilitates imputation accuracy in subsequent iterations. The proposed method provides the flexibility for researchers to conduct gap-filling solely or in combination with denoising on time series data and thus widens the applications.

When computations such as statistical simulations need to be carried out on a high performance computing (HPC) cluster, typical questions arise among researchers or practitioners. How do I interact with a HPC cluster? Do I need to type a long host name and also a password on every single login or file transfer? Why does my locally working code not run anymore on the HPC cluster? How can I install the latest versions of software on a HPC cluster to match my local setup? How can I submit a job and monitor its progress? This tutorial provides answers to such questions with experiments on an example HPC cluster.

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.

Yang et al. (2004) developed the two-dimensional principal component analysis (2DPCA) for image representation and recognition, widely used in different fields, including face recognition, biometrics recognition, cancer diagnosis, tumor classification, and others. 2DPCA has been proven to perform better and computationally more efficiently than traditional principal component analysis (PCA). However, some theoretical properties of 2DPCA are still unknown, including determining the number of principal components (PCs) in the training set, which is the critical step in applying 2DPCA. Without rigorous criteria for determining the number of PCs hampers the generalization of the application of 2DPCA. Given this issue, we propose a new method based on parallel analysis to determine the number of PCs in 2DPCA with statistical justification. Several image classification experiments demonstrate that the proposed method compares favourably to other state-of-the-art approaches regarding recognition accuracy and storage requirement, with a low computational cost.

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.

Attention mechanism has become an almost ubiquitous model architecture in deep learning. One of its distinctive features is to compute non-negative probabilistic distribution to re-weight input representations. This work reconsiders attention weights as bidirectional coefficients instead of probabilistic measures for potential benefits in interpretability and representational capacity. After analyzing the iteration process of attention scores through backwards gradient propagation, we proposed a novel activation function, TanhMax, which possesses several favorable properties to satisfy the requirements of bidirectional attention. We conduct a battery of experiments to validate our analyses and advantages of proposed method on both text and image datasets. The results show that bidirectional attention is effective in revealing input unit’s semantics, presenting more interpretable explanations and increasing the expressive power of attention-based model.

Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used to generate new samples for the minority class. However, in this paper, we challenge the common assumption that data augmentation is necessary to improve predictions on imbalanced datasets. Instead, we argue that adjusting the classifier cutoffs without data augmentation can produce similar results to oversampling techniques. Our study provides theoretical and empirical evidence to support this claim. Our findings contribute to a better understanding of the strengths and limitations of different approaches to dealing with imbalanced data, and help researchers and practitioners make informed decisions about which methods to use for a given task.

The assignment problem, crucial in various real-world applications, involves optimizing the allocation of agents to tasks for maximum utility. While it has been well-studied in the optimization literature when the underlying utilities between all agent-task pairs are known, research is sparse when the utilities are unknown and need to be learned from data on the fly. This paper addresses this gap, as motivated by mentor-mentee matching programs at many U.S. universities. We develop an efficient sequential assignment algorithm, with the aim of nearly maximizing the overall utility simultaneously over different time periods. Our proposed algorithm is to use stochastic bandit feedback to adaptively estimate the unknown utilities through linear regression models, integrating the Upper Confidence Bound (UCB) algorithm in the multi-armed bandit problem with the Hungarian algorithm in the assignment problem. We provide theoretical bounds of our algorithm for both the estimation error and the total regret. Additionally, numerical studies are also conducted to demonstrate the practical effectiveness of our algorithm.