There is growing interest in a data integration approach to survey sampling, particularly
where population registers are linked for sampling and subsequent analysis. The reason for …

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 …

We consider the multivariate linear regression model with shuffled data and additive noise,
which arises in various correspondence estimation and matching problems. We focus on the …

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 …

Is it possible to perform linear regression on datasets whose labels are shuffled with respect
to the inputs? We explore this question by proposing several estimators that recover the …

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

The supplement [10] includes all the technical proofs. In Appendix A, we first give the proof
of Theorem 3.1. In Appendix B, we give the proof of Theorem 4.1. After that, we prove …

In this paper, we study the recovery of a signal from a set of noisy linear projections
(measurements), when such projections are unlabeled, that is, the correspondence between …

Motivated by recent research on quantifying bacterial growth dynamics based on genome
assemblies, we consider a permuted monotone matrix model Y= Θ Π+ Z, where the rows …

Motivated by a series of applications in data integration, language translation,
bioinformatics, and computer vision, we consider spherical regression with two sets of unit …