Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

Missing data is a crucial issue when applying machine learning algorithms to real-world
datasets. Starting from the simple assumption that two batches extracted randomly from the …

This article provides a selective overview of the recent developments in factor models and
their applications in econometric learning. We focus on the perspective of the low-rank …

This article proposes an imputation procedure that uses the factors estimated from a tall
block along with the re-rotated loadings estimated from a wide block to impute missing …

Matrix completion is the study of recovering an underlying matrix from a sparse subset of
noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing …

Subspace estimation from unbalanced and incomplete data matrices: l2,infty statistical
guarantees Page 1 The Annals of Statistics 2021, Vol. 49, No. 2, 944–967 https://doi.org/10.1214/20-AOS1986 …

Efficient representations of data are essential for processing, exploration, and human
understanding, and Principal Component Analysis (PCA) is one of the most common …

Matrix completion is often applied to data with entries missing not at random (MNAR). For
example, consider a recommendation system where users tend to only reveal ratings for …

We consider identification and estimation with an outcome missing not at random (MNAR).
We study an identification strategy based on a so-called shadow variable. A shadow …