This paper presents a new version of the RE-EM regression tree method for longitudinal and clustered data. The RE-EM tree is a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of tree-based estimation methods. The RE-EM tree...

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in http://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit mutation clustering structure. Our results are in-sample...

This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We...

Nowcasting regards the inference on the present realization of random variables, on the basis of information available until a recent past. This paper proposes a modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times...

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite...

Team formation has been long recognized as a natural way to acquire a diverse pool of useful skills, by combining experts with complementary talents. This allows organizations to effectively complete beneficial projects from different domains, while also helping individual experts position...

Testing medical samples is expensive and pooling samples can reduce costs. But what is the optimal setup? What is the optimal pool size? We consider the simplest and most practical pooling technique and demonstrate how the correlation between samples reduces the testing cost and increases the...

The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. Clustering is the unsupervised classification of patterns into groups. However, clustering...