What is he best known for?
The fields of study he is best known for:
- Statistics
- Quantum mechanics
- Normal distribution
His primary scientific interests are in Statistics, Econometrics, Estimator, Nonparametric regression and Regression analysis.
His works in Errors-in-variables models, Semiparametric regression, Linear model, Covariate and Linear regression are all subjects of inquiry into Statistics.
His Linear model study incorporates themes from Power and Regression.
As a member of one scientific family, Raymond J. Carroll mostly works in the field of Linear regression, focusing on Generalized linear model and, on occasion, Logistic regression.
Clinical trial is closely connected to Missing data in his research, which is encompassed under the umbrella topic of Econometrics.
His studies deal with areas such as Estimation theory, Standard error and Applied mathematics as well as Estimator.
His most cited work include:
- Measurement Error in Nonlinear Models (1361 citations)
- Semiparametric Regression: Example Index (1338 citations)
- Transformation and Weighting in Regression (1164 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Statistics, Econometrics, Estimator, Regression analysis and Covariate.
Errors-in-variables models, Nonparametric regression, Nonparametric statistics, Semiparametric regression and Regression are the core of his Statistics study.
His Nonparametric regression study typically links adjacent topics like Kernel regression.
His Econometrics research incorporates elements of Inference, Logistic regression, Linear model and Missing data.
As part of one scientific family, Raymond J. Carroll deals mainly with the area of Estimator, narrowing it down to issues related to the Applied mathematics, and often Mathematical optimization.
He regularly ties together related areas like Linear regression in his Regression analysis studies.
He most often published in these fields:
- Statistics (47.28%)
- Econometrics (23.83%)
- Estimator (17.65%)
What were the highlights of his more recent work (between 2013-2021)?
- Statistics (47.28%)
- Atomic physics (7.28%)
- Covariate (13.09%)
In recent papers he was focusing on the following fields of study:
Statistics, Atomic physics, Covariate, Econometrics and Nuclear physics are his primary areas of study.
His work in Bayesian probability, Errors-in-variables models, Nonparametric statistics, Regression and Markov chain Monte Carlo is related to Statistics.
His work deals with themes such as Spectroscopy, Nucleus and Isotope, which intersect with Atomic physics.
The various areas that Raymond J. Carroll examines in his Covariate study include Sample size determination, Type I and type II errors, Homoscedasticity and Semiparametric regression, Regression analysis.
His study in Econometrics is interdisciplinary in nature, drawing from both Logistic regression, Linear regression, Independence, Estimator and Environmental exposure.
Raymond J. Carroll has included themes like Heteroscedasticity, Parametric model, Inference, Model selection and Applied mathematics in his Estimator study.
Between 2013 and 2021, his most popular works were:
- The Healthy Eating Index-2010 Is a Valid and Reliable Measure of Diet Quality According to the 2010 Dietary Guidelines for Americans (483 citations)
- Rapid publication-ready MS-Word tables for one-way ANOVA (83 citations)
- Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources (50 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Quantum mechanics
- Normal distribution
His primary areas of investigation include Statistics, Econometrics, Atomic physics, Covariate and Estimator.
His Statistics research is multidisciplinary, relying on both Variable and Variance.
His Econometrics research includes elements of Bayesian probability and Linear regression.
Raymond J. Carroll has researched Atomic physics in several fields, including Spectroscopy, Neutron, Nucleus, Isotope and Nuclide.
His Covariate research integrates issues from Spline, Semiparametric regression, Sample size determination and Confounding.
His research in Estimator intersects with topics in Two wrongs make a right, Event, Inference and Data science.
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