Lehigh-Led Researchers Develop Method That Boosts Agreement Between Forecasted and Real-World Data
An international team of mathematicians, led by Lehigh statistician Taeho Kim, has introduced an innovative method that could significantly improve how scientists make predictions, especially in fields like health, biology, and the social sciences.
The new approach is designed to make predictions that better agree with actual outcomes. Based on this idea, researchers named it the Maximum Agreement Linear Predictor, or MALP. This prediction approach achieves higher agreement in predictions by optimizing the Concordance Correlation Coefficient, or CCC, which measures how well pairs of observations fall on the 45-degree line of a scatter plot, combining both precision — how tightly points cluster — and accuracy — how close they are to the line. Traditional methods, such as the well-known least-squares approach, tend to focus solely on minimizing average errors. While effective, they can fall short when what matters most is the alignment through agreement, says Kim, assistant professor of mathematics.
“Sometimes, we don’t just want our predictions to be close—we want them to have the highest agreement with the real values,” he says. “The issue is, how can we define the agreement of two objects in a scientifically meaningful way? One way we can conceptualize this is how close the points are aligned with a 45 degree line on a scatter plot between the predicted value and the actual values. So, if the scatter plot of these shows a strong alignment with this 45 degree line, then we could say there is a good level of agreement between these two.”
Read the full story on the College of Arts and Sciences News.
Spotlight Recipient
Taeho Kim
Assistant professor