Papers Under Review Or In Revision

• Fan, Y. and James, G. (2012) "Functional Additive Regression".

• Sood, A., James, G., Tellis, G. and Zhu, J. (2012) "Patterns and Predictions in Technology Evolution: Testing SAW versus Moore, Kryder, and Gompertz".

• Fan, Y., Foutz, N., James, G. and Jank, W. (2012) "Functional Response Additive Model Estimation with Online Virtual Stock Markets". Available in PDF format.

• James, G. (2012) "Moments Based Functional Synchronization". Available in Postscript and PDF formats. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

• Tian, T. and James, G. (2012) "Interpretable Dimension Reduction for Classification with Functional Data". Available in PDF format.

Accepted and Published Journal Papers

• Radchenko, P. and James, G. (2011) "Improved Variable Selection with Forward-LASSO Adaptive Shrinkage", Annals of Applied Statistics 5, 427-448. A supplemental file containing proofs for the theorems is also available.

• Radchenko, P. and James, G. (2010) "Variable selection using Adaptive Non-linear Interaction Structures in High dimensions", Journal of the American Statistical Association 105, 1541-1553.

• Guo, J., James, G., Levina, L., Michailidis, G. and Zhu, J. (2010) "Principal Component Analysis with Sparse Fused Loadings", Journal of Computational and Graphical Statistics 19, 930-946. Available in PDF format.

• James, G., Sabatti, C., Zhou, N. and Zhu, J. (2010) "Sparse Regulatory Networks", Annals of Applied Statistics 4, 663-686.

• Tian, T., Wilcox, R. and James, G. (2010) "Data Reduction in Classification: A Simulated Annealing Based Projection Method", Statistical Analysis and Data Mining 3, 319-331. Available in PDF format.

• Tian, T., James, G. and Wilcox, R. (2010) "A Multivariate Adaptive Stochastic Search Method for Dimensionality Reduction in Classification", Annals of Applied Statistics 4, 339-364. Available in PDF format.

• 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 (PNAS) 106, 12323-12328. Available in a PDF format.

• James, G., Wang, J. and Zhu, J. (2009) "Functional Linear Regression That's Interpretable", Annals of Statistics 37, 2083-2108. Available in PDF format. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

• James, G. and Radchenko, P. (2009) "A Generalized Dantzig Selector with Shrinkage Tuning", Biometrika 96, 323-337. Available in PDF format. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

• Sood, A., James, G. and Tellis, G. (2009) "Functional Regression: A New Model for Predicting Market Penetration of New Products", Marketing Science 28, 36-51. Available in PDF format.

• 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. Available in Postscript and PDF formats.

• Radchenko, P. and James, G. (2008) "Variable Inclusion and Shrinkage Algorithms", Journal of the American Statistical Association 103, 1304-1315. Available in Postscript and PDF formats.

• James, G. (2007) "Curve Alignment by Moments", Annals of Applied Statistics 1, 480-501. Available in Postscript and PDF formats.

• 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. Available in PDF format.

• Sabatti, C. and James, G. (2006) "Bayesian Sparse Hidden Components Analysis for Transcription Regulation Networks", Bioinformatics 22, 737-744. Available in PDF format.

• James, G., and Sood, A. (2006) "Performing Hypothesis Tests on the Shape of Functional Data", Computational Statistics and Data Analysis 50, 1774-1792. Available in Postscript and PDF formats.

• James, G., and Silverman, B. (2005) "Functional Adaptive Model Estimation", Journal of the American Statistical Association 100, 565-576. Available in Postscript and PDF formats. An earlier version of the paper that contains proofs of the theorems and a medical example with sparse data is also available in Postscript and PDF formats.

• Scott, S., James, G., and Sugar, C. (2005) "Hidden Markov Models for Longitudinal Comparisons", Journal of the American Statistical Association 100, 359-369. Available in Postscript and PDF formats.

• 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. Available in Word format.

• James, G., and Sugar, C. (2003) "Clustering for Sparsely Sampled Functional Data", Journal of the American Statistical Association 98, 397-408. Available in Postscript and PDF formats. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions. A matlab version of the software (written by Simon Dablemont) can also be downloaded here.

• 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. Available in Postscript and PDF formats. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

• James, G. (2003) "Variance and Bias for General Loss Functions", Machine Learning 51, 115-135. Available in Postscript and PDF formats.

• James, G. (2002) "Generalized Linear Models with Functional Predictor Variables", Journal of the Royal Statistical Society Series B 64, 411-432. Available in Postscript and PDF formats.

• James, G., and Hastie, T. (2001) "Functional Linear Discriminant Analysis for Irregularly Sampled Curves", Journal of the Royal Statistical Society Series B 63, 533-550. Available in Postscript and PDF formats. The following Readme file explains how to download and implement the S-Plus code. There is also a matlab version of the software (written by Simon Dablemont) which can be downloaded here.

• James, G., Hastie, T., and Sugar, C. (2000) "Principal Component Models for Sparse Functional Data", Biometrika 87, 587-602. Available in Postscript and PDF formats. I also have an outline of the algorithm in Postscript and PDF formats. An R package, fpca , which implements this model using an improved fitting procedure is available from cran.

• James, G., and Hastie, T. (1998) "The Error Coding Method and PICTs", Journal of Computational and Graphical Statistics 7, 377-387. Available in Postscript and PDF formats.

Discussions

• 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. Available in PDF format.

Book Chapters

• James, G., (2010) Sparseness and Functional Data Analysis. In Oxford Handbook on Statistics and Functional Data Analysis (Editors: F. Ferraty and Y. Romain) Available in PDF format. Book available from Oxford University Press.

Conference Publications

• James, G., and Sood, A. (2005), "When Will This Technology Improve? - Hypothesis Tests On The Shape Of Functional Data ECRM 2005: The 4th European Conference on Research Methodology for Business and Management Studies

• James, G., and Hastie, T. (1998), "The Error Coding and Substitution PaCTs" Advances in Neural Information Processing Systems 10, 542-548.

Other Documents

• James, G. (1998) "Majority Vote Classifiers: Theory and Applications", Stanford University Doctoral Thesis. Available in Postscript format.

• James, G., and Hastie, T. (1997) "Error Coding and PaCTs". Available in Postscript and PDF formats. This was one of the winning papers in the 1997 ASA student paper competition for the Statistical Computing Section.