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