Research Topic · Peer-Reviewed

Statistical Models

Statistical models are mathematical representations of the relationships among variables in data, formulated to describe patterns, quantify associations, test hypotheses, and generate predictions while accounting for randomness and uncertainty. A statistical model specifies how observed data are assumed to be genera…

Curated from this journal's research 📚 7 peer-reviewed articles cited Cited 30× across the literature 🔖 ISSN 2643-2811 🗓 Reviewed July 2026

Overview

Statistical models are mathematical representations of the relationships among variables in data, formulated to describe patterns, quantify associations, test hypotheses, and generate predictions while accounting for randomness and uncertainty. A statistical model specifies how observed data are assumed to be generated, typically combining a systematic component that links explanatory variables to an outcome with a stochastic component that captures unexplained variation. Models are distinguished by purpose and structure. Regression models relate a response to one or more predictors and extend to generalised, multilevel, and survival or hazards formulations that handle different outcome types and hierarchical or time-to-event data. Time-series models, such as autoregressive integrated moving-average and harmonic regression methods, describe data ordered in time and are used for forecasting, including the prediction of disease incidence and epidemic trajectories. Machine-learning approaches, including neural networks sometimes optimised with techniques such as genetic algorithms, provide flexible, data-driven alternatives for complex prediction problems. Building and using a model involves specification, estimation of parameters from data, assessment of fit and assumptions, and validation of predictive performance, with attention to the risks of overfitting and to the distinction between association and causation. Statistical models are applied across the sciences for description, inference, and prediction, and in model-based research they formalise theory and confront it with observation. Methodological work addresses model formulation, estimation, evaluation, and forecasting.

Research published in this journal

7 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 7 articles above have been cited 30 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Statistical Models, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Model Based Research (ISSN 2643-2811).

Journal editorial board
Yoshiaki Kikuchi · Japan Yung-Yao Chen · Taiwan Yang Chen · United States

This page summarises published research for orientation; it is not medical or professional advice.