# An Ordered Lasso and Sparse Time-Lagged Regression

arXiv: https://arxiv.org/abs/1405.6447 · HTML: https://mlsanity.com/paper/1405.6447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2014.

## Built on by (most cited first, 1 of 1)

- [High-dimensional regression with potential prior information on variable importance](https://mlsanity.com/paper/2109.11281/) (2021)

## Abstract (arXiv)

We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of L1-regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main application of this idea is time-lagged regression, where we predict an outcome at time t from features at the previous K time points. In this setting it is natural to assume that the coefficients decay as we move farther away from t, and hence the order constraint is reasonable. Potential applications include financial time series and prediction of dynamic patient out- comes based on clinical measurements. We illustrate this idea on real and simulated data.