University of Southern California

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

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

PhD, Stanford University; BS, University of Auckland


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.


Chandrasekaran, D., Tellis, G. J., and James, G. (2013) "Technological Leapfrogging: When, How, and Why Emerging Markets Cathup or Pass Developed Markets in the Adoption of new Products," under second review Marketing Science.
Tian, T. S., and James, G. (2013) "Interpretable dimension reduction for classifying functional data," COMPUTATIONAL STATISTICS & DATA ANALYSIS, 57 (1), 282-296.
James, G., Witten, D., Hastie, T., and Tibshirani, R., An Introduction to Statistical Learning, Springer 2013.
Savaiano, D. A., Ritter, A. J., Klaenhammer, T. R., James, G., Longcore, A. T., Chandler, J. R., Walker, W. A., and Foyt, H. L. (2013) "Improving lactose digestion and symptoms of lactose intolerance with a novel galacto-oligosaccharide (RP-G28): a randomized, double-blind clinical trial," Nutrition Journal, 12 (1), 160.
Sood, A., James, G., Tellis, G. J., and Zhu, J. (2012) "Predicting the Path of Technological Innovation: SAW Versus Moore, Bass, Gompertz, and Kryder," Marketing Science, 31 (6), 964-979.
James, G., Sun, W., and Qiao, X. (2012) "Discussion of Clustering Random Curves Under Spatial Interdependence with Application to Service Accessibility," Technometrics, 54, 123-126.
Radchenko, P., and James, G. (2011) "Forward-Lasso with Adaptive Shrinkage," Annals of Applied Statistics, 5, 427-448.
Radchenko, P., and James, G. (2010) "Variable Selection with Adaptive Non-linear Interaction Structures in High Dimensions," Journal of the American Statistical Association, 105, 1541-1553.
Tian, S., James, G., and Wilcox, R. (2010) "A Multivariate Adaptive Stochastic Search Method for Dimensionality Reduction in Classification," Annals of Applied Statistics, 4, 339-364.
James, G., "The Oxford Handbook of Functional Data Analysis," in 2010.
James, G., and Radchenko, P. (2009) "A Generalized Dantzig Selector with Shrinkage Tuning," Biometrika, 96 (2), 323-337.
Sood, A., James, G., and Tellis, G. J. (2009) "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, 28 (1), 36-51.
Xu, M., Li, W., James, G., Mehan, M., and Zhou, X. (2009) "Automated Multi-dimensional Phenotypic Profiling Using Large Public Microarray Repositories," Proceedings of the National Academy of Sciences .
James, G., Radchenko, P., and Lv, J. (2009) "DASSO: connections between the Dantzig selector and Lasso," Journal of the Royal Statistical Society Series B, 71, 127-142.
James, G., Wang, J., and Zhu, J. (2009) "Functional Linear Regression That's Interpretable," Annals of Statistics, 37, 2083-2108.
James, G., and Radchenko, P. (2008) "Discussion of "Sure Independence Screening for Ultrahigh Dimensional Feature Space" by Fan and Lv," Journal of the Royal Statistical Society, Series B, 70, 895-896.
Radchenko, P., and James, G. (2008) "Variable Inclusion and Shrinkage Algorithms," Journal of the American Statistical Association, 103 (483), 1304-1315.
James, G. (2007) "Curve Alignment by Moments," Annals of Applied Statistics, 1, 480-501.
James, G., and Sood, A. (2006) "Performing Hypothesis Tests on the Shape of Functional Data," Computational Statistics and Data Analysis, 50, 1774-1792.
James, G., Sugar, C., Desai, R., and Rosenheck, R. (2006) "A Comparison of Outcomes Among Patients with Schizophrenia in Two Mental Health Systems: A Health State Approach," Schizophrenia Research , 86, 309-320.
Sabatti, C., and James, G. (2006) "Bayesian Sparse Hidden Components Analysis for Transcription Regulation Networks," Bioinformatics, 22, 737-744.
James, G., and Silverman, B. (2005) "Functional Adaptive Model Estimation," Journal of the American Statistical Association, 100, 565-576.
Scott, S., James, G., and Sugar, C. (2005) "Using Hidden Markov Health State Models to Analyze Data from Clinical Trials," Journal of the American Statistical Association, 100, 359-369.
Sugar, C., James, G., Lenert, L., and Rosenheck, R. (2004) "Discrete State Analysis for Interpretation of Data from Clinical Trials," Medical Care, 42, 183-196.
James, G., and Sugar, C. (2003) "Clustering for Sparsely Sampled Functional Data," Journal of the American Statistical Association, 98, 397-408.
Sugar, C., and James, G. (2003) "Finding the Number of Clusters in a Data Set: An Information Theoretic Approach," Journal of the American Statistical Association, 98, 750-763.
James, G. (2003) "Variance and Bias for General Loss Functions," Machine Learning, 51, 115-135.
James, G. (2002) "Generalized Linear Models with Functional Predictor Variables," Journal of the Royal Statistical Society, Series B, 64, 411-432.
James, G., and Hastie, T. (2001) "Functional Linear Discriminant Analysis for Irregularly Sampled Curves," Journal of the Royal Statistical Society, Series B, 63, 587-602.
James, G., Hastie, T., and Sugar, C. (2000) "Principal Component Models for Sparse Functional Data," Biometrika, 87, 587-602.
James, G., and Hastie, T. (1998) "The Error Coding Method and PICTs," Journal of Computational and Graphical Statistics, 7, 377-387.