Dynamic stochastic general equilibrium (DSGE) models have been an ubiqui...
Trend filtering is a modern approach to nonparametric regression that is...
The sparse group lasso is a high-dimensional regression technique that i...
Forecasting methodologies have always attracted a lot of attention and h...
Methods for global measurement of transcript abundance such as microarra...
For concertgoers, musical interpretation is the most important factor in...
Motivation: The discovery of relationships between gene expression
measu...
In this paper, we present methodology for estimating trends in
spatio-te...
In high-dimensional estimation, analysts are faced with more parameters ...
In this paper we analyze approximate methods for undertaking a principal...
The lasso and related sparsity inducing algorithms have been the target ...
We derive generalization error bounds for traditional time-series foreca...
Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the
gener...
In many areas of machine learning, it becomes necessary to find the
eige...
We show how to control the generalization error of time series models wh...
We derive generalization error bounds for stationary univariate
autoregr...
The literature on statistical learning for time series assumes the asymp...