It seems that I am on a lucky streak! I was also just1 awarded Lund School of Economics’s award for best thesis in 2024.
1 Used to be correct when I started drafting this post, but now it’s been a little more than a month actually.
You can read the news post here, in which I answer some questions regarding the thesis.
Here is the nomination:
In his dissertation, Johan Larsson develops new algorithms and optimization methods for sparse regression—a methodology used when the number of predictors is large relative to the number of observations. Special emphasis is placed on regularization methods, which introduce a penalty on model complexity to reduce the risk of overfitting. This is crucial in statistics and machine learning, where models with many variables often risk capturing random noise rather than meaningful signals. Regularization helps identify the most important variables, much like finding a needle in a haystack, making theoretical models more applicable to real-world data. The dissertation introduces new algorithms for sparse regression, where many predictors can be excluded without losing essential relationships, with a particular focus on regularization methods such as LASSO and SLOPE.
The thesis itself can be found here.
Again, I want to thank LUSEM and all of the people who helped me on the way.