2023 - Research.com Mathematics in United States Leader Award
1998 - Fellow of the American Statistical Association (ASA)
James Stephen Marron focuses on Statistics, Smoothing, Kernel density estimation, Applied mathematics and Estimator. The Statistics study combines topics in areas such as Rate of convergence, Econometrics and Topological data analysis. James Stephen Marron combines subjects such as Nonparametric regression, Algorithm, Kernel smoother and Kernel method with his study of Smoothing.
His studies deal with areas such as Bandwidth, Density estimation, Mathematical optimization and Cross-validation as well as Kernel density estimation. James Stephen Marron focuses mostly in the field of Applied mathematics, narrowing it down to topics relating to Mean squared error and, in certain cases, Asymptotic analysis. Within one scientific family, James Stephen Marron focuses on topics pertaining to Kernel under Estimator, and may sometimes address concerns connected to Nonparametric statistics and Selection.
James Stephen Marron mainly investigates Statistics, Artificial intelligence, Pattern recognition, Algorithm and Smoothing. His Statistics research is multidisciplinary, relying on both Econometrics and Applied mathematics. His research in Applied mathematics intersects with topics in Mean squared error and Kernel density estimation.
His study focuses on the intersection of Kernel density estimation and fields such as Kernel method with connections in the field of Probability density function. James Stephen Marron studied Artificial intelligence and Data mining that intersect with Structure. His study looks at the relationship between Smoothing and topics such as Mathematical optimization, which overlap with Bandwidth.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Statistics. His work carried out in the field of Pattern recognition brings together such families of science as Visualization, Sample size determination and Medical imaging. His Sample size determination research incorporates themes from Multivariate normal distribution and Principal component analysis.
The concepts of his Machine learning study are interwoven with issues in Decision rule and Canonical correlation. His research in the fields of Optimization problem overlaps with other disciplines such as Statistical analysis. His research investigates the connection between Statistics and topics such as Computation that intersect with problems in Data science.
James Stephen Marron mainly focuses on Artificial intelligence, Pattern recognition, Sample size determination, Machine learning and Principal component analysis. In his study, Medical imaging and Receiver operating characteristic is inextricably linked to Computer vision, which falls within the broad field of Artificial intelligence. His Sample size determination research also works with subjects such as
His Machine learning study combines topics in areas such as Data mining and Mean age. The subject of his High-dimensional statistics research is within the realm of Statistics. James Stephen Marron brings together Statistics and Statistical analysis to produce work in his papers.
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Repeated observation of breast tumor subtypes in independent gene expression data sets
Therese Sørlie;Robert Tibshirani;Joel Parker;Trevor Hastie.
Proceedings of the National Academy of Sciences of the United States of America (2003)
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Joel S. Parker;Michael Mullins;Maggie C.U. Cheang;Samuel Leung.
Journal of Clinical Oncology (2009)
Comprehensive genomic characterization of head and neck squamous cell carcinomas
Michael S. Lawrence;Carrie Sougnez;Lee Lichtenstein;Kristian Cibulskis.
Comprehensive molecular profiling of lung adenocarcinoma: The cancer genome atlas research network
Eric A. Collisson;Joshua D. Campbell;Angela N. Brooks;Angela N. Brooks;Alice H. Berger.
Comprehensive genomic characterization of squamous cell lung cancers
Peter S. Hammerman;Doug Voet;Michael S. Lawrence;Douglas Voet.
The molecular portraits of breast tumors are conserved across microarray platforms
Zhiyuan Hu;Cheng Fan;Daniel S Oh;JS Marron.
BMC Genomics (2006)
A Brief Survey of Bandwidth Selection for Density Estimation
M. C. Jones;J. S. Marron;S. J. Sheather.
Journal of the American Statistical Association (1996)
Predicting fault incidence using software change history
T.L. Graves;A.F. Karr;J.S. Marron;H. Siy.
IEEE Transactions on Software Engineering (2000)
Exact Mean Integrated Squared Error
J. S. Marron;M. P. Wand.
Annals of Statistics (1992)
Does code decay? Assessing the evidence from change management data
S.G. Eick;T.L. Graves;A.F. Karr;J.S. Marron.
IEEE Transactions on Software Engineering (2001)
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