What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Topology
Jonathan Taylor mainly investigates Lasso, Applied mathematics, Statistics, Drug resistance and Inference.
His biological study spans a wide range of topics, including Estimator, Mathematical optimization, Data mining and Null distribution.
His Applied mathematics study integrates concerns from other disciplines, such as Cover, Monotone polygon, Mathematical statistics, Regression problems and Computation.
His Drug resistance research is multidisciplinary, incorporating elements of Abacavir, Genotype, Mutation and Virology.
While the research belongs to areas of Inference, Jonathan Taylor spends his time largely on the problem of Algorithm, intersecting his research to questions surrounding Model selection, Statistical hypothesis testing and Word error rate.
His work carried out in the field of p-value brings together such families of science as Point estimation and False discovery rate.
His most cited work include:
- Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach (1113 citations)
- Random Fields and Geometry (934 citations)
- Distributed neural representation of expected value. (785 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Inference, Lasso, Statistics, Statistical hypothesis testing and Algorithm.
His study explores the link between Inference and topics such as Model selection that cross with problems in Estimator and Core.
His Lasso study combines topics in areas such as Regression, Mathematical optimization and Applied mathematics.
Regression analysis and Univariate are subfields of Statistics in which his conducts study.
His study looks at the relationship between Univariate and fields such as Feature selection, as well as how they intersect with chemical problems.
The study incorporates disciplines such as Multivariate normal distribution, False discovery rate and Pattern recognition in addition to Statistical hypothesis testing.
He most often published in these fields:
- Inference (27.23%)
- Lasso (23.04%)
- Statistics (19.37%)
What were the highlights of his more recent work (between 2016-2021)?
- Inference (27.23%)
- Algorithm (14.66%)
- Selection (14.14%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Inference, Algorithm, Selection, Lasso and Statistical hypothesis testing.
His Inference research is multidisciplinary, incorporating perspectives in Model selection, Confidence interval, Sampling, Statistics and Bayesian probability.
The various areas that Jonathan Taylor examines in his Algorithm study include Estimator, Persistent homology, Topological data analysis and Markov chain Monte Carlo.
His work deals with themes such as Statistical inference, Spatial analysis and Bump hunting, which intersect with Selection.
His study in Lasso is interdisciplinary in nature, drawing from both Sample, Generalized linear model and Interval estimation.
His Statistical hypothesis testing study combines topics from a wide range of disciplines, such as Principal component analysis and Type I and type II errors.
Between 2016 and 2021, his most popular works were:
- Post-Selection Inference for ℓ1-Penalized Likelihood Models. (57 citations)
- Communication-efficient sparse regression (49 citations)
- Selective inference with a randomized response (46 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Topology
His primary areas of investigation include Inference, Algorithm, Lasso, Selection and Statistical hypothesis testing.
His Inference study combines topics from a wide range of disciplines, such as Statistics, Model selection and Debiasing.
His research in the fields of Data point and Data collection overlaps with other disciplines such as Negative bias, Adaptive clinical trial and Scientific experiment.
His Algorithm research is multidisciplinary, relying on both Topological data analysis, Cluster analysis, Homology and Estimator, Kernel density estimation.
The various areas that Jonathan Taylor examines in his Lasso study include Sample, Generalized linear model and Confidence interval.
His studies deal with areas such as Univariate, Multivariate statistics, Centroid, Data mining and Principal component analysis as well as Statistical hypothesis testing.
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