**Gareth James**

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- Fu, L., Gang, B., James, G. and Sun, W. (2019) "Information Loss and Power Distortion from Standardizing in Multiple Hypothesis Testing "
- Derenski, J., Fan, Y. and James, G. (2019) "An Empirical Bayes Solution for Selection Bias in Functional Data"
- Fu, L., James, G. and Sun, W. (2019) "Nonparametric empirical Bayes estimation on hetrogeneous data"
- James, G. (2019) "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.

- Qiao, X., Qian, C., James, G. and Guo, S. (2019) "Doubly Functional
Graphical Models in High Dimensions",
*Biometrika*(to appear). - James, G., Paulson, C. and Rusmevichientong, P. (2019) "Penalized
and Constrained Optimization: An Application to High-Dimensional Website
Advertising",
*Journal of the American Statistical Association*(to appear). R package available from CRAN. - Qiao, X., Guo, S. and James, G. (2019) "Functional
Graphical Models",
*Journal of the American Statistical Association***114**, 211-222. - 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. - James, G. (2018) "Statistics
within Business in the Era of Big Data",
*Statistics and Probability Letters***136***,*155-159. - 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. - 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*.* - 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*.* - 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*.* - 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. - Tian, T. and James, G. (2013) "Interpretable
Dimension Reduction for Classification with Functional Data",
*Computational Statistics and Data Analysis***57**, 282-296. - James, G., Sun, W., and Qiao, X. (2012)
Discussion of "Clustering Random Curves Under Spatial Dependence'' by Serban
and Jiang
*Technometrics***54**, 123-126. - 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. - 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. - 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. - 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. - 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. - 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. - 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. - 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. - 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. - Radchenko, P. and James, G. (2008) "Variable
Inclusion and Shrinkage Algorithms",
*Journal of the American Statistical Association***103**, 1304-1315. - 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. - James, G. (2007) "Curve
Alignment by Moments",
*Annals of Applied Statistics***1**, 480-501. - 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 Sood, A. (2006) "Performing
Hypothesis Tests on the Shape of Functional Data",
*Computational Statistics and Data Analysis***50**, 1774-1792. - 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. - Scott, S., James, G., and Sugar, C. (2005) "Hidden
Markov Models for Longitudinal Comparisons",
*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. 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. 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. - 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**, 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. - 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. - James, G., and Hastie, T. (1998) "The
Error Coding Method and PICTs",
*Journal of Computational and Graphical Statistics***7**, 377-387.

- 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.

- 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.

- 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.