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Estimation in exponential family Regression based on linked data contaminated by mismatch error
Identification of matching records in multiple files can be a challengin...
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A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data
Recently, there has been significant interest in linear regression in th...
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Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing
In "Unlabeled Sensing", one observes a set of linear measurements of an ...
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A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
A tacit assumption in linear regression is that (response, predictor)-pa...
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A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression
Categorical regressor variables are usually handled by introducing a set...
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Linear Regression with Sparsely Permuted Data
In regression analysis of multivariate data, it is tacitly assumed that ...
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On Principal Components Regression, Random Projections, and Column Subsampling
Principal Components Regression (PCR) is a traditional tool for dimensio...
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Regularization-free estimation in trace regression with symmetric positive semidefinite matrices
Over the past few years, trace regression models have received considera...
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Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields
Consider a random vector with finite second moments. If its precision ma...
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Matrix factorization with Binary Components
Motivated by an application in computational biology, we consider low-ra...
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Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
Least squares fitting is in general not useful for high-dimensional line...
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