What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Larry Wasserman spends much of his time researching Statistics, Algorithm, Prior probability, False discovery rate and Artificial intelligence.
His work in Nonparametric statistics, Statistical hypothesis testing, Mathematical statistics, Lasso and Bayes' theorem are all subfields of Statistics research.
His Algorithm study incorporates themes from Graph, Sample size determination, Inference, Graphical model and Calculus.
His Prior probability research incorporates elements of Posterior probability, Density estimation and Bayesian inference.
His Posterior probability research integrates issues from Mathematical optimization, Bernstein–von Mises theorem, Likelihood function and Applied mathematics.
His Artificial intelligence study integrates concerns from other disciplines, such as Additive model, Machine learning, Linear model and Pattern recognition.
His most cited work include:
- Computing Bayes Factors by Combining Simulation and Asymptotic Approximations (1968 citations)
- All of Statistics: A Concise Course in Statistical Inference (1112 citations)
- The Selection of Prior Distributions by Formal Rules (965 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Statistics, Algorithm, Nonparametric statistics, Applied mathematics and Artificial intelligence.
His research brings together the fields of False discovery rate and Statistics.
The concepts of his Algorithm study are interwoven with issues in Inference, Cluster analysis, Kernel density estimation, Mixture model and Gaussian.
His study in Nonparametric statistics is interdisciplinary in nature, drawing from both Estimator and Parametric statistics.
His research in Applied mathematics focuses on subjects like Nonparametric regression, which are connected to Smoothing.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
He most often published in these fields:
- Statistics (20.00%)
- Algorithm (18.54%)
- Nonparametric statistics (13.90%)
What were the highlights of his more recent work (between 2015-2021)?
- Minimax (10.73%)
- Algorithm (18.54%)
- Applied mathematics (13.41%)
In recent papers he was focusing on the following fields of study:
Larry Wasserman mostly deals with Minimax, Algorithm, Applied mathematics, Inference and Artificial intelligence.
His Algorithm study incorporates themes from Nonparametric statistics, Model selection and Cluster analysis.
His studies deal with areas such as Dimension, Test statistic, Nonparametric regression, Smoothness and Kernel density estimation as well as Applied mathematics.
His Inference research includes elements of Statistical inference, Regression, Statistics, Estimator and Frequentist inference.
As part of his studies on Statistics, Larry Wasserman frequently links adjacent subjects like Type.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
Between 2015 and 2021, his most popular works were:
- Topological Data Analysis (192 citations)
- Distribution-Free Predictive Inference for Regression (141 citations)
- Detecting effects of filaments on galaxy properties in the Sloan Digital Sky Survey III (78 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Larry Wasserman mainly focuses on Inference, Applied mathematics, Regression, Manifold and Minimax.
Larry Wasserman has researched Inference in several fields, including Kernel, Statistical inference, Model selection, Mathematical optimization and Eigenvalues and eigenvectors.
His biological study deals with issues like Algorithm, which deal with fields such as Cluster analysis, Bootstrapping and Linear regression.
His study explores the link between Applied mathematics and topics such as Kernel density estimation that cross with problems in Mixture model and Smoothing.
His Nonparametric regression research focuses on Function and how it connects with Statistical hypothesis testing.
Linear discriminant analysis is the subject of his research, which falls under Statistics.
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