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Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration.
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D. Hsu, K. Shi, and X. Sun · 2017
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A. Pananjady, M. Wainwright, and T. Cortade · 2017
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A. Abid and J. Zou · 2018
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Hypothesis test for normal mixture models: The EM approach
J. Chen and P. Li · 2009
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An overview of composite likelihood estimation
C. Varin, N. Reid, and D. Firth · 2011
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M. H. P. Hof and A. H. Zwinderman · 2012
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Sampling and reconstruction of spatial fields using mobile sensors
J. Unnikrishnan and M. Vetterli · 2013
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Compressed sensing with unknown sensor permutation
V. Emiya, A. Bonnefoy, L. Daudet, and R. Gribonval · 2014
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Linear regression with shuffled data: Statistical and computational limits of permutation recovery
A. Pananjady, M. Wainwright, and T. Cortade · 2018
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Spherical regresion under mismatch corruption with application to automated knowledge translation
X. Shi, X. Lu, and T. Cai · 2018
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Eigenspace conditions for homomorphic sensing
M. Tsakiris · 2018
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J. Unnikrishnan, S. Haghighatshoar, and M. Vetterli · 2018
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Uncoupled isotonic regression via minimum Wasserstein deconvolution
P. Rigollet and J. Weed · 2019
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Linear regression with sparsely permuted data
M. Slawski and E. Ben-David · 2019
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A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
M. Slawski, E. Ben-David, and P. Li · 2019
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A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels
M. Slawski, M. Rahmani, and Ping Li · 2019
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Permutation recovery from multiple measurement vectors in unlabeled sensing
H. Zhang, M. Slawski, and P. Li · 2019
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