Gareth James

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Working Papers

• Rava, B., Sun, W., James, G. and Tong, X. (2022) "A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification".

• Derenski, J., Fan, Y. and James, G. (2022) "An Empirical Bayes Solution for Selection Bias in Functional Data". A supplementary file containing proofs of the theretical results is available here and a longer version of the paper is available here.

• Banerjee, B., Fu, L., James, G. and Sun, W. (2022) "Nonparametric Empirical Bayes Estimation on Hetrogeneous Data"  

• James, G. (2022) "Moments Based Functional Synchronization". The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

Refereed Journal Papers and Discussions

1. Yao, S., Rava, B., Tong, X. and James, G. (2022) "Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm", Journal of the American Statistical Association (to appear).

2. Fu, L., Gang, B., James, G. and Sun, W. (2022) "Heterocedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing", Journal of the American Statistical Association (to appear).  

3. James, G., Radchenko, P. and Rava, B. (2022) "Irrational Exuberance: Correcting Bias in Probability Estimates", Journal of the American Statistical Association 117, 455-468. R package available from CRAN and Python package available at PyPi.

4. Chandrasekaran, D., Tellis, G. and James, G. (2022) "Leapfrogging, Cannibalization, and Survival during Disruptive Technological Change: The Critical Role of Rate of Disengagement", Journal of Marketing 86, 149-166. 

5. Qiao, X., Qian, C., James, G. and Guo, S. (2020) "Doubly Functional Graphical Models in High Dimensions", Biometrika 107, 415-431.

6. James, G., Paulson, C. and Rusmevichientong, P. (2020) "Penalized and Constrained Optimization: An Application to High-Dimensional Website Advertising", Journal of the American Statistical Association 115, 107-122. R package available from CRAN.

7. Qiao, X., Guo, S. and James, G. (2019) "Functional Graphical Models", Journal of the American Statistical Association 114, 211-222.

8. Paulson, C., Luo, L. and James, G. (2018) "Efficient Large-Scale Internet Media Selection Optimization for Online Display Advertising", Journal of Marketing Research 55, 489-506. There is also an online appendix and an R package to implement the method is available at CRAN. A story about this project.

9. James, G. (2018) "Statistics within Business in the Era of Big Data", Statistics and Probability Letters 136, 155-159.

10. Derenski, J., Fan, Y. and James, G. (2017) Discussion of "Random-projection ensemble classification" by Cannings and Samworth, Journal of the Royal Statistical Society, Series B 70 , 895-896.

11. Fan, Y., James, G. and Radchenko, P. (2015) "Functional Additive Regression", Annals of Statistics 43, 2296-2325. Supplimentary material cantaining proofs of some of the theorems is available here.

12. Radchenko, P., Qiao, X. and  James, G. (2015) "Index Models for Sparsely Sampled Functional Data", Journal of the American Statistical Association 110, 824-836.Supplimentary material cantaining proofs of some of the theorems is available here.

13. Fan, Y., Foutz, N., James, G. and Jank, W. (2014) "Functional Response Additive Model Estimation with Online Virtual Stock Markets", Annals of Applied Statistics 8, 2435-2460.

14. Savaiano, D., Ritter, A., Klaenhammer, T., James, G., Longcore, A., Chandler, J., Walker, W., and Foyt, H. (2013) "Improving lactose digestion and symptoms of lactose intolerance with a novel galactooligosaccharide (RP-G28): a randomized, double-blind clinical trial", Nutrition Journal 12:160, 1-9.

15. Tian, T. and James, G. (2013) "Interpretable Dimension Reduction for Classification with Functional Data", Computational Statistics and Data Analysis 57, 282-296.

16. James, G., Sun, W., and Qiao, X. (2012) Discussion of "Clustering Random Curves Under Spatial Dependence'' by Serban and Jiang Technometrics 54, 123-126. 

17. Sood, A., James, G., Tellis, G. and Zhu, J. (2012) "Predicting the Path of Technology Innovation: SAW Versus Moore, Bass, Gompertz and Kryder", Marketing Science 31, 964-979.

18. 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.

19. 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.

20. 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.

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

22. 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.

23. 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.

24. 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.

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

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

27. 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.

28. 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.

29. Radchenko, P. and James, G. (2008) "Variable Inclusion and Shrinkage Algorithms", Journal of the American Statistical Association 103, 1304-1315.

30. 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.

31. James, G. (2007) "Curve Alignment by Moments", Annals of Applied Statistics 1, 480-501.

32. 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.

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

34. James, G., and Sood, A. (2006) "Performing Hypothesis Tests on the Shape of Functional Data", Computational Statistics and Data Analysis 50, 1774-1792.

35. James, G., and Silverman, B. (2005) "Functional Adaptive Model Estimation", Journal of the American Statistical Association 100, 565-576. Click here for an earlier version of the paper that contains proofs of the theorems and a medical example with sparse data. R code, curtesy of Xiaomeng Ju, to run FAME is available here. Use the example.R script to test out the code. Further details on the code here.

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

37. 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.

38. James, G., and Sugar, C. (2003) "Clustering for Sparsely Sampled Functional Data", Journal of the American Statistical Association 98, 397-408. 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.

39. 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. The R code to implement this procedure can be downloaded here. See the documentation for instructions on installing and using the functions.

40. James, G. (2003) "Variance and Bias for General Loss Functions", Machine Learning 51, 115-135.

41. James, G. (2002) "Generalized Linear Models with Functional Predictor Variables", Journal of the Royal Statistical Society Series B 64, 411-432.

42. 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. 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.

43. James, G., Hastie, T., and Sugar, C. (2000) "Principal Component Models for Sparse Functional Data", Biometrika 87, 587-602. Click here for an outline of the algorithm. An R package, fpca , which implements this model using an improved fitting procedure is available from cran.

44. James, G., and Hastie, T. (1998) "The Error Coding Method and PICTs", Journal of Computational and Graphical Statistics 7, 377-387.

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). 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.

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