What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
Art B. Owen mainly investigates Statistics, Monte Carlo method, Gene, Latin hypercube sampling and Gene expression.
As part of his studies on Statistics, Art B. Owen often connects relevant areas like Anomaly detection.
As a part of the same scientific study, Art B. Owen usually deals with the Monte Carlo method, concentrating on Square-integrable function and frequently concerns with Calculus, Hypercube and Binary logarithm.
His Latin hypercube sampling study also includes fields such as
- Monte Carlo integration and related Applied mathematics,
- Combinatorics most often made with reference to Quasi-Monte Carlo method.
His Empirical likelihood study integrates concerns from other disciplines, such as Marginal likelihood, Confidence region, Likelihood function and Restricted maximum likelihood.
His Likelihood-ratio test study combines topics in areas such as Estimating equations and Multivariate random variable.
His most cited work include:
- Empirical likelihood ratio confidence intervals for a single functional (1609 citations)
- Empirical Likelihood Ratio Confidence Regions (1146 citations)
- Empirical Likelihood for Linear Models (611 citations)
What are the main themes of his work throughout his whole career to date?
Art B. Owen mostly deals with Statistics, Algorithm, Monte Carlo method, Combinatorics and Applied mathematics.
His study involves Statistical hypothesis testing, Estimator, Confidence interval, Control variates and Empirical likelihood, a branch of Statistics.
His research on Algorithm also deals with topics like
- Resampling most often made with reference to Heteroscedasticity,
- Random effects model that connect with fields like Generalized linear mixed model and Consistency.
His Monte Carlo method research incorporates elements of Discrete mathematics and Numerical integration.
The study incorporates disciplines such as Bounded function and Random variable in addition to Combinatorics.
The Applied mathematics study combines topics in areas such as Linear regression and Computer experiment.
He most often published in these fields:
- Statistics (30.26%)
- Algorithm (18.46%)
- Monte Carlo method (18.46%)
What were the highlights of his more recent work (between 2017-2021)?
- Statistics (30.26%)
- Applied mathematics (14.87%)
- Monte Carlo method (18.46%)
In recent papers he was focusing on the following fields of study:
Art B. Owen spends much of his time researching Statistics, Applied mathematics, Monte Carlo method, Quasi-Monte Carlo method and Algorithm.
His work on Statistics deals in particular with Efficiency, Regression discontinuity design, Experimental data, Observational study and Estimator.
His Applied mathematics research includes themes of Sampling, Generalization and Random effects model.
His Monte Carlo method research incorporates themes from Numerical integration, Pseudorandom number generator and Probabilistic method.
The concepts of his Quasi-Monte Carlo method study are interwoven with issues in Unit cube, Kernel density estimation, Density estimation and Mathematical analysis.
In the field of Algorithm, his study on State overlaps with subjects such as Application areas, Improved method and Decision threshold.
Between 2017 and 2021, his most popular works were:
- Deterministic parallel analysis: an improved method for selecting factors and principal components (24 citations)
- Importance sampling the union of rare events with an application to power systems analysis (12 citations)
- Optimizing the tie-breaker regression discontinuity design (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
Art B. Owen focuses on Statistics, Algorithm, Experimental data, Observational study and Law of large numbers.
Regression discontinuity design, Parametric model, Nonparametric regression, Regression and Efficiency are the primary areas of interest in his Statistics study.
His research in Algorithm intersects with topics in Meta-analysis, Subgroup analysis and Null hypothesis.
His Experimental data research integrates issues from Covariate, Randomized controlled trial, Delta method, Propensity score matching and External validity.
His Observational study research is multidisciplinary, incorporating perspectives in Sensitivity, Estimator, Shrinkage, Risk Estimate and Confounding.
His work deals with themes such as Discrete mathematics and Net, which intersect with Law of large numbers.
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