124 citations · 550 across the 52 of their papers we have counts for
5 papers · 1 filter
Linear Non-Gaussian Component Analysis via Maximum Likelihood
Benjamin B. Risk, David S. Matteson, David Ruppert
Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component…
VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables
William Nicholson, David Matteson, Jacob Bien
The vector autoregression (VAR) has long proven to be an effective method for modeling the joint dynamics of macroeconomic time series as well as forecasting. A major shortcoming o…
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach
Zhengyi Zhou, David S. Matteson
Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typi…
Predicting Melbourne Ambulance Demand using Kernel Warping
Zhengyi Zhou, David S. Matteson
Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data spar…
Change Points via Probabilistically Pruned Objectives
Nicholas A. James, David S. Matteson
The concept of homogeneity plays a critical role in statistics, both in its applications as well as its theory. Change point analysis is a statistical tool that aims to attain homo…