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Found 5 papers, 4 papers with code
Covariate-guided Bayesian mixture model for multivariate time series
In this paper, we propose a group-based method to cluster a collection of multivariate time series via a Bayesian mixture of smoothing splines.
RISE: Robust Individualized Decision Learning with Sensitive Variables
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is prohibited due to reasons such as delayed availability or fairness concerns.
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data.
A sparse negative binomial mixture model for clustering RNA-seq count data
With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with Gaussian assumption.
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion (MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed.
