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- Larry Wasserman

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
H-index
72
Citations
26,077
217
World Ranking
107
National Ranking
61

2016 - Member of the National Academy of Sciences

2010 - Fellow of the American Association for the Advancement of Science (AAAS)

1999 - COPSS Presidents' Award

1996 - Fellow of the American Statistical Association (ASA)

- 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.

- 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)

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.

- Statistics (20.00%)
- Algorithm (18.54%)
- Nonparametric statistics (13.90%)

- Minimax (10.73%)
- Algorithm (18.54%)
- Applied mathematics (13.41%)

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.

- 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)

- 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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Computing Bayes Factors by Combining Simulation and Asymptotic Approximations

Thomas J. Diciccio;Robert E. Kass;Adrian Raftery;Larry Wasserman.

Journal of the American Statistical Association **(1997)**

1980 Citations

All of Statistics: A Concise Course in Statistical Inference

Larry Wasserman.

**(2014)**

1785 Citations

The Selection of Prior Distributions by Formal Rules

Robert E. Kass;Larry Wasserman.

Journal of the American Statistical Association **(1996)**

1593 Citations

All of Nonparametric Statistics

Larry Wasserman.

**(2008)**

1426 Citations

A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

Robert E. Kass;Larry Wasserman.

Journal of the American Statistical Association **(1995)**

1412 Citations

All Of Statistics

Larry Wasserman.

**(2004)**

971 Citations

Bayesian model selection and model averaging

Larry Wasserman.

Journal of Mathematical Psychology **(2000)**

963 Citations

An overview of robust Bayesian analysis

James O. Berger;Elías Moreno;Luis Raul Pericchi;M. Jesús Bayarri.

Test **(1994)**

679 Citations

Operating characteristics and extensions of the false discovery rate procedure

Christopher Genovese;Larry Wasserman.

Journal of The Royal Statistical Society Series B-statistical Methodology **(2002)**

653 Citations

Practical Bayesian Density Estimation Using Mixtures of Normals

Kathryn Roeder;Larry Wasserman.

Journal of the American Statistical Association **(1997)**

651 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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