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Leveraging Survey Metadata for LLM Reasoning via Knowledge Graphs

https://jds-online.org/journal/JDS/article/1490

Published: May 20, 2026 21:00

Statistical survey metadata contains essential contextual information that underpins the accurate interpretation, discovery, and reuse of statistical data. However, traditional metadata formats are not optimized for consumption by large language models…

Editorial: Bridging Disciplines: Advancing AI, Statistics, and Data Science Together

https://jds-online.org/journal/JDS/article/1489

Published: May 11, 2026 21:00

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Semiparametric Dynamic Copula Models using Rolling-window Portfolio Optimization

https://jds-online.org/journal/JDS/article/1488

Published: May 7, 2026 21:00

The mean-variance portfolio model, based on the risk-return trade-off for optimal asset allocation, remains fundamental in portfolio optimization. However, its reliance on restrictive assumptions about asset return distributions limits its applicability to…

Interpretable Word-Level Context-Based Sentiment Analysis

https://jds-online.org/journal/JDS/article/1487

Published: May 6, 2026 21:00

We propose a fine-grained attention-based multiple instance classification (FAMIC) model for interpretable word-level sentiment analysis (SA) using only document-level sentiment labels. By operating at the word level, FAMIC enhances interpretability while…

Designing Accessible and Dependable Tools for Vocational Rehabilitation Data Analysis

https://jds-online.org/journal/JDS/article/1486

Published: May 4, 2026 21:00

The U.S. Rehabilitation Services Administration (RSA) has partnered with state vocational rehabilitation (VR) agencies since 1973 to improve employment outcomes for individuals with disabilities. A critical resource in this effort is the RSA-911 dataset, a…

Relative Growth Modeling of Anthropometric Outcomes

https://jds-online.org/journal/JDS/article/1485

Published: April 2, 2026 21:00

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…

Enhance Supervised Self-Organization Clustering by Utilizing Unsupervised Learning Embeddings on Discrete Data

https://jds-online.org/journal/JDS/article/1483

Published: March 31, 2026 21:00

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…

Quantifying Direct and Indirect Effects Through Joint Modeling of Terminal Events and Gap Times Between Recurrent Events

https://jds-online.org/journal/JDS/article/1484

Published: March 31, 2026 21:00

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…

Clusters, Trends, and Choices: Feature Selection in Interactive Statistical Graphics

https://jds-online.org/journal/JDS/article/1480

Published: March 25, 2026 22:00

This study investigates how user ability to manipulate plot features affects graphical perception, by extending a previous graphical study (Vanderplas and Hofmann, 2017) with an interactive framework. Similar to the original study, statistical lineups…

Data-Driven Model Structure Diagrams for Hierarchical Linear Mixed Models

https://jds-online.org/journal/JDS/article/1481

Published: March 25, 2026 22:00

Hierarchical linear mixed models are commonly used in many scientific fields. However, without a strong statistical background, it can be hard to understand the relationships between the random effect variables and the inferences that can be made when a…

Predicting Business Cycles Using Deep Learning Models

https://jds-online.org/journal/JDS/article/1482

Published: March 25, 2026 22:00

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…

Investigating Spatial Dependence in the Degree of Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains

https://jds-online.org/journal/JDS/article/1479

Published: March 11, 2026 22:00

Satellite precipitation products have the potential to be employed for the purpose of better understanding extreme precipitation events in remote mountainous terrain, where weather stations and radar data tend to be sparse. For this reason, it is crucial…