Grabit: Gradient tree-boosted Tobit models for default prediction

Insurance Premium Prediction via Gradient Tree- Boosted Tweedie Compound Poisson Models

Boosted Varying-Coefficient Regression Models for Product Demand Prediction

Estimating the aggregated market demand for a product in a dynamic market is critical to manufacturers and retailers. Motivated by the need for a statistical demand prediction model for laptop pricing at Hewlett-Packard, we have developed a novel boosting-based varying-coefficient regression model. The developed model uses regression trees as the base learner, and is generally applicable to var...

Blinder-Oaxaca Decomposition for Tobit Models

Blinder-Oaxaca Decomposition for Tobit Models In this paper, a decomposition method for Tobit-models is derived, which allows the differences in a censored outcome variable between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. The method is applied to a decomposition of t...

Default Prediction of Various Structural Models

The structural approach of credit risk modeling has gained growing attention in both academics and industry. While there has been increasing effort in testing the structural models for credit derivatives pricing, little result has been shown on the default prediction of the structural models. In this paper, we compare comprehensively various credit structural models for their default prediction...

Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning

Labeled training data is an essential part of any supervised machine learning framework. In practice, there is a trade-off between the quality of a label and its cost. In this paper, we consider a problem of learning to rank on a large-scale dataset with low-quality relevance labels aiming at maximizing the quality of a trained ranker on a small validation dataset with high-quality ground truth...