Journal of Data Science

The presence of outliers in a dataset can substantially bias the results of statistical analyses. In general, micro edits are often performed manually on all records to correct for outliers. A set of constraints and decision rules is used to simplify the editing process. However, agricultural data collected through repeated surveys are characterized by complex relationships that make revision and vetting challenging. Therefore, maintaining high data-quality standards is not sustainable in short timeframes. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has partially automated its editing process to improve the accuracy of final estimates. NASS has investigated several methods to modernize its anomaly detection system because simple decision rules may not detect anomalies that break linear relationships. In this article, a computationally efficient method that identifies format-inconsistent, historical, tail, and relational anomalies at the data-entry level is introduced. Four separate scores (i.e., one for each anomaly type) are computed for all nonmissing values in a dataset. A distribution-free method motivated by the Bienaymé-Chebyshev’s inequality is used for scoring the data entries. Fuzzy logic is then considered for combining four individual scores into one final score to determine the outliers. The performance of the proposed approach is illustrated with an application to NASS survey data.

The North American Product Classification System (NAPCS) was first introduced in the 2017 Economic Census and provides greater detail on the range of products and services offered by businesses than what was previously available with just an industry code. In the 2022 Economic Census, NAPCS consisted of 7,234 codes and respondents often found that they were unable to identify correct NAPCS codes for their business, leaving instead written descriptions of their products and services. Over one million of these needed to be reviewed by Census analysts in the 2017 Economic Census. The Smart Instrument NAPCS Classification Tool (SINCT) offers respondents a low latency search engine to find appropriate NAPCS codes based on a written description of their products and services. SINCT uses a neural network document embedding model (doc2vec) to embed respondent searches in a numerical space and then identifies NAPCS codes that are close to the search text. This paper shows one way in which machine learning can improve the survey respondent experience and reduce the amount of expensive manual processing that is necessary after data collection. We also show how relatively simple tools can achieve an estimated 72% top-ten accuracy with thousands of possible classes, limited training data, and strict latency requirements.

This paper presents an in-depth analysis of patterns and trends in the open-source software (OSS) contributions by the U.S. federal government agencies. OSS is a unique category of computer software notable for its publicly accessible source code and the rights it provides for modification and distribution for any purpose. Prompted by the Federal Source Code Policy (USCIO, 2016), Code.gov was established as a platform to facilitate the sharing of custom-developed software across various federal government agencies. This study leverages data from Code.gov, which catalogs OSS projects developed and shared by government agencies, and enhances this data with detailed development and contributor information from GitHub. By adopting a cost estimation methodology that is consistent with the U.S. national accounting framework for software investment proposed in Korkmaz et al. (2024), this research provides annual estimates of investment in OSS by government agencies for the 2009–2021 period. The findings indicate a significant investment by the federal government in OSS, with the 2021 investment estimated at around $407 million. This study not only sheds light on the government’s role in fostering OSS development but also offers a valuable framework for assessing the scope and value of OSS initiatives within the public sector.

The coronavirus disease 2019 (COVID-19) pandemic presented unique challenges to the U.S. healthcare system, particularly for nonprofit U.S. hospitals that are obligated to provide community benefits in exchange for federal tax exemptions. We sought to examine how hospitals initiated, modified, or disbanded community benefits programming in response to the COVID-19 pandemic. We used the free-response text in Part IV of Internal Revenue Service (IRS) Form 990 Schedule H (F990H) to assess health equity and disparities. We combined traditional key term frequency and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering approaches with a novel Generative Pre-trained Transformer (GPT) 3.5 summarization approach. Our research reveals shifts in community benefits programming. We observed an increase in COVID-related terms starting in the 2019 tax year, indicating a pivot in community focus and efforts toward pandemic-related activities such as telehealth services and COVID-19 testing and prevention. The clustering analysis identified themes related to COVID-19 and community benefits. Generative Artificial Intelligence (GenAI) summarization with GPT3.5 contextualized these changes, revealing examples of healthcare system adaptations and program cancellations. However, GPT3.5 also encountered some accuracy and validation challenges. This multifaceted text analysis underscores the adaptability of hospitals in maintaining community health support during crises and suggests the potential of advanced AI tools in evaluating large-scale qualitative data for policy and public health research.

Missing data is a common occurrence in various fields, spanning social science, education, economics, and biomedical research. Disregarding missing data in statistical analyses can introduce bias to study outcomes. To mitigate this issue, imputation methods have proven effective in reducing nonresponse bias and generating complete datasets for subsequent analysis of secondary data. The efficacy of imputation methods hinges on the assumptions of the underlying imputation model. While machine learning techniques such as regression trees, random forest, XGBoost, and deep learning have demonstrated robustness against model misspecification, their optimal performance may necessitate fine-tuning under specific conditions. Moreover, imputed values generated by these methods can sometimes deviate unnaturally, falling outside the normal range. To address these challenges, we propose a novel Predictive Mean Matching imputation (PMM) procedure that leverages popular machine learning-based methods. PMM strikes a balance between robustness and the generation of appropriate imputed values. In this paper, we present our innovative PMM approach and conduct a comparative performance analysis through Monte Carlo simulation studies, assessing its effectiveness against other established methods.

Changepoint analysis has had a striking variety of applications, and a rich methodology has been developed. Our contribution here is a new approach that uses nonlinear regression analysis as an intermediate computational device. The tool is quite versatile, covering a number of different changepoint scenarios. It is largely free of parametric model assumptions, and has the major advantage of providing standard errors for formal statistical inference. Both abrupt and gradual changes are covered.

There is growing interest in accommodating network structure in panel data models. We consider dynamic network Poisson autoregressive (DN-PAR) models for panel count data, enabling their use in regard to a time-varying network structure. We develop a Bayesian Markov chain Monte Carlo technique for estimating the DN-PAR model, and conduct Monte Carlo experiments to examine the properties of the posterior quantities and compare dynamic and constant network models. The Monte Carlo results indicate that the bias in the DN-PAR models is negligible, while the constant network model suffers from bias when the true network is dynamic. We also suggest an approach for extracting the time-varying network from the data. The empirical results for the count data for confirmed cases of COVID-19 in the United States indicate that the extracted dynamic network models outperform the constant network models in regard to the deviance information criterion and out-of-sample forecasting.

Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example.

A joint equivalence and difference (JED) test is needed because difference tests and equivalence (more exactly, similarity) tests each provide only a one-sided answer. The concept and underlying theory have appeared numerous times, noted and discussed here, but never in a form usable in workaday statistical applications. This work provides such a form as a straightforward simple test with a step-by-step guide and possible interpretations and formulas. For initial treatment, it restricts attention to a t test of two means. The guide is illustrated by a numerical example from the field of orthopedics. To assess the quality of the JED test, its sensitivity and specificity are examined for test outcomes depending on error risk α, total sample size, sub-sample size ratio, and variability ratio. These results are shown in tables. Interpretations are discussed. It is concluded that the test exhibits high power and effect size and that only quite small samples show any effect on the power or effect size of the JED test by commonly seen values of any of the parameters. Data for the example and computer codes for using the JED test are accessible through links to supplementary material. We recommend that this work be extended to other test forms and multivariate forms.

The National Association of Stock Car Auto Racing (NASCAR) is ranked among the top ten most popular sports in the United States. NASCAR events are characterized by on-track racing punctuated by pit stops since cars must refuel, replace tires, and modify their setup throughout a race. A well-executed pit stop can allow drivers to gain multiple seconds on their opponents. Strategies around when to pit and what to perform during a pit stop are under constant evaluation. One currently unexplored area is publically available communication between each driver and their pit crew during the race. Due to the many hours of audio, manual analysis of even one driver’s communications is prohibitive. We propose a fully automated approach to analyze driver–pit crew communication. Our work was conducted in collaboration with NASCAR domain experts. Audio communication is converted to text and summarized using cluster-based Latent Dirichlet Analysis to provide an overview of a driver’s race performance. The transcript is then analyzed to extract important events related to pit stops and driving balance: understeer (pushing) or oversteer (over-rotating). Named entity recognition (NER) and relationship extraction provide context to each event. A combination of the race summary, events, and real-time race data provided by NASCAR are presented using Sankey visualizations. Statistical analysis and evaluation by our domain expert collaborators confirmed we can accurately identify important race events and driver interactions, presented in a novel way to provide useful, important, and efficient summaries and event highlights for race preparation and in-race decision-making.