What is he best known for?
The fields of study he is best known for:
- Statistics
- Regression analysis
- Mathematical analysis
His primary scientific interests are in Nonparametric statistics, Econometrics, Statistics, Nonparametric regression and Categorical variable.
His study in Nonparametric statistics is interdisciplinary in nature, drawing from both Smoothing, Estimator and Parametric model.
His work in Estimator covers topics such as Mean squared error which are related to areas like Mathematical optimization, Conditional expectation, Quadratic programming and Additive smoothing.
The Econometrics study which covers Parametric statistics that intersects with Gross domestic product and Stochastic dominance.
Jeffrey S. Racine interconnects Kernel method, Kernel, Semiparametric regression, Algorithm and Minimum-variance unbiased estimator in the investigation of issues within Nonparametric regression.
In Categorical variable, he works on issues like Kernel regression, which are connected to Quantile function, Quantile, Quantile regression and Regular conditional probability.
His most cited work include:
- Nonparametric Econometrics: Theory and Practice (959 citations)
- Nonparametric Econometrics: The np Package (584 citations)
- Nonparametric estimation of regression functions with both categorical and continuous data (540 citations)
What are the main themes of his work throughout his whole career to date?
Jeffrey S. Racine focuses on Nonparametric statistics, Econometrics, Estimator, Statistics and Categorical variable.
Jeffrey S. Racine does research in Nonparametric statistics, focusing on Nonparametric regression specifically.
Jeffrey S. Racine focuses mostly in the field of Econometrics, narrowing it down to topics relating to Parametric statistics and, in certain cases, Probit.
His studies deal with areas such as Polynomial regression, Mean squared error, Applied mathematics, Sample and Monte Carlo method as well as Estimator.
His work on Kernel regression, Kernel smoother, Heteroscedasticity and Regression analysis as part of general Statistics research is frequently linked to Conditional variance, bridging the gap between disciplines.
The Categorical variable study combines topics in areas such as Multinomial distribution and Multivariate adaptive regression splines.
He most often published in these fields:
- Nonparametric statistics (54.65%)
- Econometrics (51.16%)
- Estimator (37.21%)
What were the highlights of his more recent work (between 2017-2021)?
- Nonparametric statistics (54.65%)
- Econometrics (51.16%)
- Algorithm (11.63%)
In recent papers he was focusing on the following fields of study:
Jeffrey S. Racine mostly deals with Nonparametric statistics, Econometrics, Algorithm, Kernel and Inference.
His Nonparametric statistics study deals with the bigger picture of Statistics.
The concepts of his Econometrics study are interwoven with issues in Markdown and Regression.
His research integrates issues of Function, Kernel density estimation, Applied mathematics and Probability mass function in his study of Kernel.
His Kernel density estimation research is multidisciplinary, relying on both Polynomial kernel and Categorical variable.
His Inference research incorporates themes from Monte Carlo method, Multivariate statistics and Model selection.
Between 2017 and 2021, his most popular works were:
- Nonparametric Estimation and Inference for Panel Data Models (5 citations)
- Nonparametric Estimation and Inference for Panel Data Models (5 citations)
- Nonparametric Estimation and Inference for Panel Data Models (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Regression analysis
Jeffrey S. Racine mainly investigates Nonparametric statistics, Econometrics, Regression, Estimator and Random effects model.
The various areas that Jeffrey S. Racine examines in his Nonparametric statistics study include Kernel smoother, Econometric model and Partial derivative.
Statistics covers Jeffrey S. Racine research in Kernel smoother.
His Econometrics research integrates issues from Inference and Graphics.
His Regression research is multidisciplinary, incorporating perspectives in Panel data, Kernel, Spline, B-spline and Generalized least squares.
Jeffrey S. Racine has researched Estimator in several fields, including Set, Simple, Categorical variable, Applied mathematics and Monte Carlo method.
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