307 citations · 541 across the 12 of their papers we have counts for
15 papers
The Projected Covariance Measure for assumption-lean variable significance testing
Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah +1
Testing the significance of a variable or group of variables for predicting a response , given additional covariates , is a ubiquitous task in statistics. A simple but co…
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalise…
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders
Yuhao Wang, Rajen D. Shah
We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some f…
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
Qingyuan Zhao, Nianqiao Ju, Sergio Bacallado +1
The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation perio…
Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties
Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani
We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding…
Double-estimation-friendly inference for high-dimensional misspecified models
Rajen D. Shah, Peter Bühlmann
All models may be wrong -- but that is not necessarily a problem for inference. Consider the standard -test for the significance of a variable for predicting response wh…
Goodness-of-fit testing in high-dimensional generalized linear models
Jana Janková, Rajen D. Shah, Peter Bühlmann +1
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or…
RSVP-graphs: Fast High-dimensional Covariance Matrix Estimation under Latent Confounding
Rajen D. Shah, Benjamin Frot, Gian-Andrea Thanei +1
In this work we consider the problem of estimating a high-dimensional covariance matrix , given observations of confounded data with covariance , where…
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
Rajen D. Shah, Jonas Peters
It is a common saying that testing for conditional independence, i.e., testing whether whether two random vectors and are independent, given , is a hard statistical prob…
The xyz algorithm for fast interaction search in high-dimensional data
Gian-Andrea Thanei, Nicolai Meinshausen, Rajen D. Shah
When performing regression on a dataset with variables, it is often of interest to go beyond using main linear effects and include interactions as products between individual v…
Goodness of fit tests for high-dimensional linear models
Rajen D. Shah, Peter Bühlmann
In this work we propose a framework for constructing goodness of fit tests in both low and high-dimensional linear models. We advocate applying regression methods to the scaled res…
On b-bit min-wise hashing for large-scale regression and classification with sparse data
Rajen D. Shah, Nicolai Meinshausen
Large-scale regression problems where both the number of variables, , and the number of observations, , may be large and in the order of millions or more, are becoming increa…
Random Intersection Trees
Rajen Dinesh Shah, Nicolai Meinshausen
Finding interactions between variables in large and high-dimensional datasets is often a serious computational challenge. Most approaches build up interaction sets incrementally, a…
Modelling Interactions in High-dimensional Data with Backtracking
Rajen D. Shah
We study the problem of high-dimensional regression when there may be interacting variables. Approaches using sparsity-inducing penalty functions such as the Lasso can be useful fo…
Variable selection with error control: Another look at Stability Selection
Rajen D. Shah, Richard J. Samworth
Stability Selection was recently introduced by Meinshausen and Buhlmann (2010) as a very general technique designed to improve the performance of a variable selection algorithm. It…