Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2307.13748

Time Series Analysis Applied to Notifications of Work Accidents

Tony Gabriel-Fernandez Cayo·Fred Torres-Cruz
View original source ↗Read source document ↗Export metadata ↓

Abstract

Time series analysis applied to occupational accident reports is a powerful tool for understanding the evolution of occupational accidents over time. It provides valuable information to make informed decisions. In this study, data from reports of work accidents collected from the MINISTRY OF LABOR AND EMPLOYMENT PROMOTION. MTPE were analyzed by time series. Significant patterns and trends in accident reporting have been identified, leading to more effective prevention strategies and better health and safety management.

Explore related subjects

stat.OT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tony Gabriel-Fernandez Cayo, Fred Torres-Cruz. 2023-07-25. Time Series Analysis Applied to Notifications of Work Accidents. https://arxiv.org/abs/2307.13748

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game

We present an interactive classroom activity designed to address a central challenge in teaching introductory Bayesian statistics: how to formalize subjective knowledge and available information into prior distributions and then update them with empirical data. Role-playing as data analysts for a racing team, students evaluate candidate tires by converting qualitative engineering reports into prior distributions, collecting primary data via a virtual racing game, and using a Beta-Binomial model to inform team strategy. This discovery-based exercise allows small groups to observe directly how different prior choices and sample data jointly shape posterior inference. Student feedback ($n=32$) highlights high enjoyment, engagement and improved conceptual clarity. Open-access materials to implement the activity are provided, alongside recommendations for adapting it to other teaching contexts.

stat.OT↗
arXiv2026

Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods

Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on $N = 606$ data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written recommendation of regularization methods increases trust or intended use. We find no evidence of such an effect. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.

stat.OT↗
arXiv2026

Exact analysis of a split--merge queue with latent Erlang-factor dependent subtask times

This paper studies a two-server split--merge queue with positively dependent subtask service times modeled through a latent-factor bivariate Erlang construction. An exact characterization of the split--merge completion time is obtained, including explicit formulas for its first two moments and the resulting mean waiting time. Under fixed marginal service-time distributions, independence is shown to stochastically increase the completion time and hence overestimate mean waiting time. Numerical illustrations show that this benchmark gap can be substantial.

stat.OT↗