7 citations · 21 across the 10 of their papers we have counts for
10 papers
Inference conditional on selection: a review
Anna Neufeld, Ronan Perry, Daniela Witten
In this article, we review selective inference, a set of techniques for inference when the statistical question asked is a function of the data. This setting often arises in contem…
Post-selection inference for penalized M-estimators via score thinning
Ronan Perry, Snigdha Panigrahi, Daniela Witten
We consider inference for M-estimators after model selection using a sparsity-inducing penalty. While existing methods for this task require bespoke inference procedures, we propos…
On the minimum strength of (unobserved) covariates to overturn an insignificant result
Danielle Tsao, Ronan Perry, Carlos Cinelli
We study conditions under which the addition of variables to a regression equation can turn a previously statistically insignificant result into a significant one. Specifically, we…
Infer-and-widen, or not?
Ronan Perry, Zichun Xu, Olivia McGough +1
In recent years, there has been substantial interest in the task of selective inference: inference on a parameter that is selected from the data. Many of the existing proposals fal…
Inference on the proportion of variance explained in principal component analysis
Ronan Perry, Snigdha Panigrahi, Jacob Bien +1
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low…
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf
Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated…
mvlearn: Multiview Machine Learning in Python
Ronan Perry, Gavin Mischler, Richard Guo +9
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years. However,…
Universally Consistent K-Sample Tests via Dependence Measures
Sambit Panda, Cencheng Shen, Ronan Perry +4
The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical met…
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Adam Li, Ronan Perry, Chester Huynh +6
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learn…
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Ronan Perry, Ronak Mehta, Richard Guo +6
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for co…