8 papers
Testing hypotheses via orthogonalization
Ameer Dharamshi, Runjia Zou, Daniela Witten
Classical hypothesis testing frameworks break down in contemporary settings in which null hypotheses are increasingly abstract, the same data are used to both generate and test hyp…
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…
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…
Generalized Prediction-Powered Inference, with Application to Binary Classifier Evaluation
Runjia Zou, Daniela Witten, Brian Williamson
In the partially-observed outcome setting, a recent set of proposals known as "prediction-powered inference" (PPI) involve (i) applying a pre-trained machine learning model to pred…
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…
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…