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Gareth James
Vice Dean for Faculty and Academic Affairs & Professor of Data Science and Operations

USC Marshall School of Business
Los Angeles, CA 90089-0808

Phone: 
213-740-9696
gareth@usc.edu
Education: 
PhD, Stanford University; BS, University of Auckland
Personal Website: 
http://www-rcf.usc.edu/~gareth

Overview

Gareth James is an expert on statistical methodology with particular application to marketing problems such as prediction of technology evolution. He has published over 20 articles in leading journals such as the Journal of the American Statistical Association, for which he also serves on the editorial review board. He teaches both MBA and PhD courses ranging from introductory statistics through to advanced modern non-linear regression techniques. Professor James has won several recent research and teaching awards, including the Deans award for Research Excellence and two Golden Apple awards for his MBA courses.

Research

Predicting the Path of Technological Innovation: SAW Versus Moore, Bass, Gompertz, and Kryder • 2012
Discussion of Clustering Random Curves Under Spatial Interdependence with Application to Service Accessibility • 2012
Forward-Lasso with Adaptive Shrinkage • 2011
Variable Selection with Adaptive Non-linear Interaction Structures in High Dimensions • 2010
A Multivariate Adaptive Stochastic Search Method for Dimensionality Reduction in Classification • 2010
The Oxford Handbook of Functional Data Analysis • 2010
A Generalized Dantzig Selector with Shrinkage Tuning • 2009
Automated Multi-dimensional Phenotypic Profiling Using Large Public Microarray Repositories • 2009
DASSO: connections between the Dantzig selector and Lasso • 2009
Functional Linear Regression That's Interpretable • 2009
Functional Regression: A New Model for Predicting Market Penetration of New Products • 2009
Discussion of "Sure Independence Screening for Ultrahigh Dimensional Feature Space" by Fan and Lv • 2008
Variable Inclusion and Shrinkage Algorithms • 2008
Curve Alignment by Moments • 2007
A Comparison of Outcomes Among Patients with Schizophrenia in Two Mental Health Systems: A Health State Approach • 2006
Bayesian Sparse Hidden Components Analysis for Transcription Regulation Networks • 2006
Performing Hypothesis Tests on the Shape of Functional Data • 2006
Functional Adaptive Model Estimation • 2005
Using Hidden Markov Health State Models to Analyze Data from Clinical Trials • 2005
Discrete State Analysis for Interpretation of Data from Clinical Trials • 2004
Finding the Number of Clusters in a Data Set: An Information Theoretic Approach • 2003
Clustering for Sparsely Sampled Functional Data • 2003
Variance and Bias for General Loss Functions • 2003
Generalized Linear Models with Functional Predictor Variables • 2002
Functional Linear Discriminant Analysis for Irregularly Sampled Curves • 2001
Principal Component Models for Sparse Functional Data • 2000
The Error Coding Method and PICTs • 1998