36 citations · 212 across the 30 of their papers we have counts for
34 papers
Sparse reconstruction of ordinary differential equations with inference
Sara Venkatraman, Sumanta Basu, Martin T. Wells
Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) frame…
Statistical inference for association studies in the presence of binary outcome misclassification
Kimberly A. Hochstedler Webb, Martin T. Wells
In biomedical and public health association studies, binary outcome variables may be subject to misclassification, resulting in substantial bias in effect estimates. The feasibilit…
K-ARMA Models for Clustering Time Series Data
Derek O. Hoare, David S. Matteson, Martin T. Wells
We present an approach to clustering time series data using a model-based generalization of the K-Means algorithm which we call K-Models. We prove the convergence of this general a…
Interpretable Latent Variables in Deep State Space Models
Haoxuan Wu, David S. Matteson, Martin T. Wells
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data. The model estimate…
Clustering Structure of Microstructure Measures
Liao Zhu, Ningning Sun, Martin T. Wells
This paper builds the clustering model of measures of market microstructure features which are popular in predicting stock returns. In a 10-second time-frequency, we study the clus…
On Graphical Models and Convex Geometry
Haim Bar, Martin T. Wells
We introduce a mixture-model of beta distributions to identify significant correlations among predictors when is large. The method relies on theorems in convex geometry, wh…