307 citations · 541 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…