Suppose that we have a regression problem with response variable $ Y\in\mathbb {R}^ d $
and predictor $ X\in\mathbb {R}^ d $, for $ d\ge 1$. In permuted or unlinked regression we …

Consider a noisy linear observation model with an unknown permutation, based on
observing y= Π* Ax*+ w, where x*∈ ℝ d is an unknown vector, Π* is an unknown nxn …

Data are often collected from multiple heterogeneous sources and are combined
subsequently. In combing data, record linkage is an essential task for linking records in …

Isotonic regression is a standard problem in shape-constrained estimation where the goal is
to estimate an unknown non-decreasing regression function from independent pairs where …

In regression analysis of multivariate data, it is tacitly assumed that response and predictor
variables in each observed response-predictor pair correspond to the same entity or unit. In …

This article considers algorithmic and statistical aspects of linear regression when the
correspondence between the covariates and the responses is unknown. First, a fully …

A tacit assumption in linear regression is that (response, predictor)-pairs correspond to
identical observational units. A series of recent works have studied scenarios in which this …

Unlabeled linear regression, or “linear regression with an unknown permutation”, has
attracted increasing attentions due to its applications in (eg,) linkage record and de …

A recent line of research termed" unlabeled sensing" and" shuffled linear regression" has
been exploring under great generality the recovery of signals from subsampled and …

Linear regression without correspondences is the problem of performing a linear regression
fit to a dataset for which the correspondences between the independent samples and the …