NEW HOT PAPERS

Why do you think your paper is highly cited?

This paper reveals some fundamental properties of the L1 (lasso) method which has been, during the last six years, one of the most popular topics in statistics, machine learning, and signal processing. It proves some limitations of the classical lasso and provides an improved version named the “adaptive lasso.” This paper also connects the L1 penalization and the non-concave penalization for the first time. The later is another “hot” model selection method.

Does it describe a new discovery, methodology, or synthesis of knowledge?

We closely examined the theoretical properties of the classical lasso and discovered some fundamental limitations. We then further proposed a new method, named the adaptive lasso, which can remove all the limitations by a simple modification on the classical lasso.

Would you summarize the significance of your paper in layman’s terms?

It is critically important to have an efficient and powerful method for feature selection in order to extract useful knowledge from massive high-dimensional data. We invented a method that is theoretically sound and computationally efficient for performing feature selection with high-dimensional data.

How did you become involved in this research, and were there any problems along the way?

Our research was partly motivated by a conjecture raised by Jianqing Fan and Runze Li, in their article: “Variable selection via nonconcave penalized likelihood and its. oracle properties” (J. Amer. Statist. Assoc. [96]: 1348-1360, 2001.) One contribution of our paper is that we rigorously proved their conjecture.

Where do you see your research leading in the future?

The current variable selection methods often assume some strong model structures. We need new methodology and theory to help remove such rigid assumptions.

Do you foresee any social or political implications for your research?

Conceptually, our method should be able to be applied to all data-analytic problems where variable selection is needed. For instance, in many medical studies, researchers want to discover new bio-markers or identify a set of genes that may cause a specific disease.

Hui Zou, Assistant Professor
School of Statistics
University of Minnesota
Minneapolis, MN, USA