8 citations · 10 across the 12 of their papers we have counts for
14 papers
Conditional Independence Testing in Time Series
Jieru Shi, Rajen D. Shah
We consider the problem of testing Granger causality in time series, specifically, whether the future outcome and the exposure history are conditionally indepe…
The Debiased Score Test: Hunt-and-test for Semiparametric Hypotheses
Aditya Dhawan, F. Richard Guo, Rajen D. Shah
The parametric score test assesses a hypothesis through derivatives of the log-likelihood, whose expectation vanishes under the null. When the parameter of interest is a regression…
Aggregation of Statistical Evidence under Exchangeability
Antonin Schrab, Rajen Shah, Arthur Gretton +1
We study aggregation of statistical evidence under unknown and potentially complex dependence using group-invariance. Building on permutation-based constructions that treat transfo…
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…
Average partial effect estimation using double machine learning
Harvey Klyne, Rajen D. Shah
Single-parameter summaries of variable effects in regression settings are desirable for ease of interpretation. However (partially) linear models for example, which would deliver t…
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…
Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values
F. Richard Guo, Rajen D. Shah
Many testing problems are readily amenable to randomised tests such as those employing data splitting. However despite their usefulness in principle, randomised tests have obvious…
Change-point regression with a smooth additive disturbance
Florian Pein, Rajen D. Shah
We assume a nonparametric regression model where the signal is given by the sum of a piecewise constant function and a smooth function. To detect the change-points and estimate the…
Cross-validation for change-point regression: pitfalls and solutions
Florian Pein, Rajen D. Shah
Cross-validation is the standard approach for tuning parameter selection in many non-parametric regression problems. However its use is less common in change-point regression, perh…
High-dimensional regression with potential prior information on variable importance
Benjamin G. Stokell, Rajen D. Shah
There are a variety of settings where vague prior information may be available on the importance of predictors in high-dimensional regression settings. Examples include ordering on…
Structure Learning for Directed Trees
Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann +1
Knowing the causal structure of a system is of fundamental interest in many areas of science and can aid the design of prediction algorithms that work well under manipulations to t…