36 citations · 212 across the 30 of their papers we have counts for
14 papers · 1 filter
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…
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…
Mixed Effect Modeling and Variable Selection for Quantile Regression
Haim Bar, James Booth, Martin T. Wells
It is known that the estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights…
Quantile Regression Modelling via Location and Scale Mixtures of Normal Distributions
Haim Y. Bar, James G. Booth, Martin T. Wells
We show that the estimating equations for quantile regression can be solved using a simple EM algorithm in which the M-step is computed via weighted least squares, with weights com…