What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
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.
His most cited work include:
- Repeated observation of breast tumor subtypes in independent gene expression data sets (4338 citations)
- Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes (2823 citations)
- Comprehensive molecular profiling of lung adenocarcinoma: The cancer genome atlas research network (2762 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Statistics (26.55%)
- Artificial intelligence (23.30%)
- Pattern recognition (15.34%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (23.30%)
- Pattern recognition (15.34%)
- Machine learning (8.55%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- Comprehensive molecular profiling of lung adenocarcinoma: The cancer genome atlas research network (2762 citations)
- Comprehensive genomic characterization of head and neck squamous cell carcinomas (1933 citations)
- Characterization of HPV and host genome interactions in primary head and neck cancers. (200 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
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
- Dimension together with Covariance, Smoothing and Mathematical statistics,
- Statistical hypothesis testing together with Risk factor, Orthodontics and Case-control study.
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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