What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Internal medicine
Bradley Efron mainly investigates Statistics, Econometrics, Nonparametric statistics, Jackknife resampling and Confidence interval.
His study on Statistics is mostly dedicated to connecting different topics, such as Word error rate.
His Jackknife resampling study combines topics in areas such as Resampling, Delta method, Random variable and Combinatorics.
His Combinatorics research focuses on subjects like Sampling distribution, which are linked to Bootstrap aggregating.
His studies in Confidence interval integrate themes in fields like Calibration and Statistical hypothesis testing.
The various areas that Bradley Efron examines in his Inference study include Bootstrap confidence interval, Simar and Sampling theory.
His most cited work include:
- An introduction to the bootstrap (35495 citations)
- Bootstrap Methods: Another Look at the Jackknife (12691 citations)
- Least angle regression (6995 citations)
What are the main themes of his work throughout his whole career to date?
Bradley Efron mainly focuses on Statistics, Econometrics, Bayes' theorem, Frequentist inference and Statistical hypothesis testing.
Confidence interval, Estimator, Nonparametric statistics, Standard error and Jackknife resampling are the subjects of his Statistics studies.
His work in Econometrics is not limited to one particular discipline; it also encompasses Regression analysis.
Bradley Efron usually deals with Bayes' theorem and limits it to topics linked to Exponential family and Likelihood function.
The Frequentist inference study combines topics in areas such as Statistical inference, Inference and Data science.
His study in Inference is interdisciplinary in nature, drawing from both Data mining and False discovery rate.
He most often published in these fields:
- Statistics (37.01%)
- Econometrics (18.15%)
- Bayes' theorem (17.79%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (9.96%)
- Statistics (37.01%)
- Bayes' theorem (17.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Statistics, Bayes' theorem, Inference and Machine learning.
He integrates Statistics and R package in his research.
His Bayes' theorem research integrates issues from Exponential family, Applied mathematics, Class and Stability.
His Inference research is multidisciplinary, incorporating perspectives in Deconvolution, Measure, Frequentist inference and Maximum likelihood, Likelihood function.
His work in Frequentist inference covers topics such as Statistical inference which are related to areas like Econometrics.
His work on Model selection, Boosting and Feature vector as part of general Machine learning study is frequently linked to Oracle, bridging the gap between disciplines.
Between 2015 and 2021, his most popular works were:
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (151 citations)
- Effect of Perioperative Gabapentin on Postoperative Pain Resolution and Opioid Cessation in a Mixed Surgical Cohort: A Randomized Clinical Trial. (82 citations)
- Empirical Bayes deconvolution estimates (53 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Internal medicine
His primary areas of investigation include Bayes' theorem, Inference, Estimation, Random forest and Econometrics.
Bradley Efron has researched Bayes' theorem in several fields, including Measure, Class, Stability and Frequentist inference.
His Frequentist inference research includes themes of Relevance and Bayes analysis.
His Inference research incorporates themes from Markov chain Monte Carlo, Sufficient statistic, Curvature, Applied mathematics and Machine learning.
His Estimation research spans across into subjects like Attribution and Black box.
His Bayesian probability research is multidisciplinary, incorporating elements of Algorithm, Statistical inference, Data science and Big data.
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