The Impossible Dream: Why Regression Confidence Bands Can’t Exist Without Assumptions
Why the intuitive idea of regression confidence bands breaks down under mathematical scrutiny.
Recent writing
Handling Non-Stationarity in Time Series Data: Techniques and Best Practices
Non-stationarity is one of the biggest challenges in time series analysis. Explore proven techniques and statistical tools to transform non-stationary data into model-ready series.
Time Series Forecasting with SARIMA: Seasonal ARIMA Explained
This in-depth guide explores Seasonal ARIMA (SARIMA) for forecasting time series with seasonal components. Learn parameter tuning, interpretation, and Python implementation with real-world examples.
Nonlinear Growth Models in Macroeconomics
Nonlinear growth models offer a richer and more realistic framework for understanding macroeconomic development over time. This article explores the mathematical structures and real-world relevance of non-linear dynamics in economic growth theory.
Differential Equations in Growth Models
Differential equations are essential in modeling economic growth, providing insight into long-term trends and the impact of policy changes on macroeconomic variables.
Improving Elderly Mental Health with Machine Learning and Data Analytics
Machine learning is reshaping elderly mental health care. This article explores how data-driven insights help detect depression, track mood changes, and identify early signs of cognitive decline.
Bayesian State Space Models in Macroeconometrics
Explore the critical role of Bayesian state space models in macroeconometric analysis, with a focus on linear Gaussian models, dimension reduction, and non-linear or non-Gaussian extensions.
Understanding Statistical Significance in Data Analysis
Learn the essential concepts of statistical significance and how it applies to data analysis and business decision-making.
Multi-Agent Collaboration in Finance: Building Intelligent Teams with LLMs
Multi-agent systems are redefining how financial tasks like M&A analysis can be approached, using teams of collaborative LLMs with distinct responsibilities.
Predicting Hospital Readmissions for Elderly Patients Using Machine Learning
Machine learning models are revolutionizing post-hospitalization care by predicting hospital readmissions in elderly patients, helping healthcare providers optimize treatment and reduce complications.