2023 - Research.com Mathematics in United States Leader Award
2022 - Research.com Mathematics in United States Leader Award
2013 - Fellow of the American Association for the Advancement of Science (AAAS)
1988 - COPSS Presidents' Award
1982 - Fellow of the American Statistical Association (ASA)
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 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.
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.
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.
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.
Measurement error in nonlinear models: a modern perspective
Raymond J. Carroll.
Transformation and Weighting in Regression
R. J. Carroll;D. Ruppert.
Semiparametric Regression: Example Index
David Ruppert;M. P. Wand;R. J. Carroll.
Prognostic factors for local recurrence, metastasis, and survival rates in squamous cell carcinoma of the skin, ear, and lip : implications for treatment modality selection
Dan E. Rowe;Dan E. Rowe;Raymond J. Carroll;Calvin L. Day.
Journal of The American Academy of Dermatology (1992)
Measurement Error in Nonlinear Models
Raymond J. Carroll;David Ruppert;Leonard A. Stefanski.
Hyperproinsulinaemia in obese fat/fat mice associated with a carboxypeptidase E mutation which reduces enzyme activity
Jürgen K. Naggert;Lloyd D. Fricker;Oleg Varlamov;Patsy M. Nishina.
Nature Genetics (1995)
Generalized Partially Linear Single-Index Models
R. J. Carroll;Jianqing Fan;Irène Gijbels;M. P. Wand.
Journal of the American Statistical Association (1997)
Variance Function Estimation
Marie Davidian;Raymond J. Carroll.
Journal of the American Statistical Association (1987)
Structure of Dietary Measurement Error: Results of the OPEN Biomarker Study
Victor Kipnis;Amy F. Subar;Douglas Midthune;Laurence S. Freedman.
American Journal of Epidemiology (2003)
Long‐Term Recurrence Rates in Previously Untreated (Primary) Basal Cell Carcinoma: Implications for Patient Follow‐Up
Dan E. Rowe;Raymond J. Carroll;Calvin L. Jr. Day.
The Journal of Dermatologic Surgery and Oncology (1989)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: