4 papers
Outrigger local polynomial regression
Elliot H. Young, Rajen D. Shah, Richard J. Samworth
Standard local polynomial estimators of a nonparametric regression function employ a weighted least squares loss function that is tailored to the setting of homoscedastic Gaussian…
Latent confounding in high-dimensional nonlinear models
Yuhao Wang, Rajen Shah
We consider the the problem of identifying causal effects given a high-dimensional treatment vector in the presence of low-dimensional latent confounders. We assume a parametric st…
Sandwich regression for accurate and robust estimation in generalized linear multilevel and longitudinal models
Elliot H. Young, Rajen D. Shah
Generalized linear models are a popular tool in applied statistics, with their maximum likelihood estimators enjoying asymptotic Gaussianity and efficiency. As all models are wrong…
ROSE Random Forests for Robust Semiparametric Efficient Estimation
Elliot H. Young, Rajen D. Shah
It is widely recognised that semiparametric efficient estimation can be hard to achieve in practice: estimators that are in theory efficient may require unattainable levels of accu…