49 citations · 78 across the 7 of their papers we have counts for
8 papers
Compressed and Penalized Linear Regression
Darren Homrighausen, Daniel J. McDonald
Modern applications require methods that are computationally feasible on large datasets but also preserve statistical efficiency. Frequently, these two concerns are seen as contrad…
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets
Darren Homrighausen, Daniel J. McDonald
In this paper we analyze approximate methods for undertaking a principal components analysis (PCA) on large data sets. PCA is a classical dimension reduction method that involves t…
A study on tuning parameter selection for the high-dimensional lasso
Darren Homrighausen, Daniel J. McDonald
High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical gua…
Risk-consistency of cross-validation with lasso-type procedures
Darren Homrighausen, Daniel J. McDonald
The lasso and related sparsity inducing algorithms have been the target of substantial theoretical and applied research. Correspondingly, many results are known about their behavio…
Efficient Estimators for Sequential and Resolution-Limited Inverse Problems
Darren Homrighausen, Christopher R. Genovese
A common problem in the sciences is that a signal of interest is observed only indirectly, through smooth functionals of the signal whose values are then obscured by noise. In such…
Leave-one-out cross-validation is risk consistent for lasso
Darren Homrighausen, Daniel J. McDonald
The lasso procedure is ubiquitous in the statistical and signal processing literature, and as such, is the target of substantial theoretical and applied research. While much of thi…