4 papers
Flexible semiparametric modeling with application to Causal Inference
Kun Ren, Wen Su, Li Liu +2
This paper proposes a flexible new framework for constructing Neyman-orthogonal scores in semiparametric models involving infinite-dimensional nuisance parameters. While locally es…
Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors
Shuhao Jiao, Hernando Ombao, Ian W. McKeague
It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how pr…
Hybrid combinations of parametric and empirical likelihoods
Nils Lid Hjort, Ian W. McKeague, Ingrid Van Keilegom
This paper develops a hybrid likelihood (HL) method based on a compromise between parametric and nonparametric likelihoods. Consider the setting of a parametric model for the distr…
Coefficient Shape Transfer Learning for Functional Linear Regression
Shuhao Jiao, Ian W. Mckeague
The shapes of functions provide highly interpretable summaries of their trajectories. This article develops a novel transfer learning methodology to tackle the challenge of data sc…