Rajen D. Shah | DeepAI

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∙ 08/17/2023

Average partial effect estimation using double machine learning

Single-parameter summaries of variable effects are desirable for ease of...

0 Harvey Klyne, et al. ∙

∙ 07/21/2023

Sandwich Boosting for Accurate Estimation in Partially Linear Models for Grouped Data

We study partially linear models in settings where observations are arra...

0 Elliot H. Young, et al. ∙

∙ 01/06/2023

Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values

Many testing problems are readily amenable to randomised tests such as t...

0 F. Richard Guo, et al. ∙

∙ 11/03/2022

The Projected Covariance Measure for assumption-lean variable significance testing

Testing the significance of a variable or group of variables X for predi...

0 Anton Rask Lundborg, et al. ∙

∙ 12/06/2021

Cross-validation for change-point regression: pitfalls and solutions

Cross-validation is the standard approach for tuning parameter selection...

0 Florian Pein, et al. ∙

∙ 09/23/2021

High-dimensional regression with potential prior information on variable importance

There are a variety of settings where vague prior information may be ava...

0 Benjamin G. Stokell, et al. ∙

∙ 08/19/2021

Structure Learning for Directed Trees

Knowing the causal structure of a system is of fundamental interest in m...

11 Martin Emil Jakobsen, et al. ∙

∙ 01/18/2021

Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis

We study the problem of testing the null hypothesis that X and Y are con...

0 Anton Rask Lundborg, et al. ∙

∙ 11/17/2020

Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders

We consider estimation of average treatment effects given observational ...

0 Yuhao Wang, et al. ∙

∙ 02/28/2020

Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties

We propose a method for estimation in high-dimensional linear models wit...

0 Benjamin G. Stokell, et al. ∙

∙ 09/24/2019

Double-estimation-friendly inference for high-dimensional misspecified models

All models may be wrong—but that is not necessarily a problem for infere...

0 Rajen D. Shah, et al. ∙

∙ 08/09/2019

Goodness-of-fit testing in high-dimensional generalized linear models

We propose a family of tests to assess the goodness-of-fit of a high-dim...

0 Jana Jankova, et al. ∙

∙ 11/02/2018

RSVP-graphs: Fast High-dimensional Covariance Matrix Estimation under Latent Confounding

In this work we consider the problem of estimating a high-dimensional p ...

0 Rajen D. Shah, et al. ∙

∙ 04/19/2018

The Hardness of Conditional Independence Testing and the Generalised Covariance Measure

It is a common saying that testing for conditional independence, i.e., t...

0 Rajen D. Shah, et al. ∙

∙ 10/17/2016

The xyz algorithm for fast interaction search in high-dimensional data

When performing regression on a dataset with p variables, it is often of...

0 Gian-Andrea Thanei, et al. ∙

∙ 08/06/2013

On b-bit min-wise hashing for large-scale regression and classification with sparse data

Large-scale regression problems where both the number of variables, p, a...

0 Rajen D. Shah, et al. ∙

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