4 papers · 1 filter
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…
Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
Elliot H. Young, Peter Bühlmann
We develop Clustered Random Forests, a random forests algorithm for clustered data, arising from independent groups that exhibit within-cluster dependence. The leaf-wise prediction…
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…
Sandwich Boosting for Accurate Estimation in Partially Linear Models for Grouped Data
Elliot H. Young, Rajen D. Shah
We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear mode…