Our paper A Tree-Based Semi-Varying Coefficient Model for the COM-Poisson Distribution" is now published online at the Journal of Computational & Graphical Statistics. This was part of Suneel Chatla's PhD dissertation at NTHU (he is now faculty at UT El Paso).

In the paper, we propose a tree-based semi-varying coefficient model for the Conway–Maxwell–Poisson (CMP) distribution. The advantage of tree-based methods is their scalability to high-dimensional data. We develop CMPMOB, an estimation procedure for a semi-varying coefficient model, using model-based recursive partitioning (MOB). The proposed framework is broader than the existing MOB framework as it allows node-invariant effects to be included in the model. To simplify the computational burden of the exhaustive search employed in the original MOB algorithm, a new split point estimation procedure is proposed by borrowing tools from change point estimation methodology. The proposed method uses only the estimated score functions without fitting models for each split point and, therefore, is computationally simpler. Since the tree-based methods only provide a piece-wise constant approximation to the underlying smooth function, we further propose the CMPBoost semi-varying coefficient model which uses the gradient boosting procedure for estimation. The usefulness of the proposed methods are illustrated using simulation studies and a real example from a bike sharing system in Washington, DC.

This link gives 50 free copies to the paper.

POSTPONED DUE TO COVID19

On March 23, 2020 I'll deliver a public talk on Behavioral Big Data and Healthcare Research in Melbourne, Australia, for the Australian Research Council Center of Excellence for Mathematical and Statistical Frontiers.

For more details and registration.