What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Mathematical analysis
The scientist’s investigation covers issues in Statistics, Estimator, Applied mathematics, Asymptotic distribution and Nonparametric regression.
Oliver Linton has researched Statistics in several fields, including Inference and Econometrics.
His Estimator research incorporates elements of Nonparametric statistics, Conditional probability distribution and Monte Carlo method.
His research in Applied mathematics intersects with topics in Smoothing, Mathematical optimization, Sample, Generalized method of moments and Multivariate kernel density estimation.
His Asymptotic distribution study integrates concerns from other disciplines, such as Statistical hypothesis testing, Least absolute deviations, Independent and identically distributed random variables, Normal distribution and Moment.
The study incorporates disciplines such as Kernel regression, Semiparametric regression and Heteroscedasticity in addition to Nonparametric regression.
His most cited work include:
- A kernel method of estimating structured nonparametric regression based on marginal integration (441 citations)
- Consistent Testing for Stochastic Dominance under General Sampling Schemes (345 citations)
- Estimation of Semiparametric Models when the Criterion Function Is Not Smooth (318 citations)
What are the main themes of his work throughout his whole career to date?
Oliver Linton mostly deals with Estimator, Econometrics, Applied mathematics, Nonparametric statistics and Statistics.
His study in Asymptotic distribution and Nonparametric regression is carried out as part of his studies in Estimator.
His work in Asymptotic distribution tackles topics such as Null hypothesis which are related to areas like Predictability and Test statistic.
He regularly ties together related areas like Inference in his Econometrics studies.
His Applied mathematics study which covers Covariance that intersects with Kronecker product.
His studies deal with areas such as Parametric statistics, Additive model, Covariate, Kernel density estimation and Series as well as Nonparametric statistics.
He most often published in these fields:
- Estimator (47.54%)
- Econometrics (39.39%)
- Applied mathematics (33.14%)
What were the highlights of his more recent work (between 2015-2021)?
- Econometrics (39.39%)
- Estimator (47.54%)
- Applied mathematics (33.14%)
In recent papers he was focusing on the following fields of study:
Econometrics, Estimator, Applied mathematics, Nonparametric statistics and Statistics are his primary areas of study.
His studies in Econometrics integrate themes in fields like Multivariate statistics and Inference.
The various areas that Oliver Linton examines in his Estimator study include Covariance matrix, Pointwise and Consistency.
His Applied mathematics research integrates issues from Sample size determination, Covariance, Test statistic, Central limit theorem and Mathematical optimization.
His Nonparametric statistics study also includes fields such as
- Heteroscedasticity which intersects with area such as Monte Carlo method,
- Unit root and related Additive model.
His study on Nonparametric regression is often connected to Geography as part of broader study in Statistics.
Between 2015 and 2021, his most popular works were:
- Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series (25 citations)
- Classification of non‐parametric regression functions in longitudinal data models (19 citations)
- Additive nonparametric models with time variable and both stationary and nonstationary regressions (14 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Mathematical analysis
His primary areas of study are Estimator, Econometrics, Applied mathematics, Statistics and Nonparametric statistics.
His study in Estimator focuses on Asymptotic distribution and Nonparametric regression.
The Econometrics study combines topics in areas such as Conditional probability and Inference.
His work carried out in the field of Applied mathematics brings together such families of science as Covariance, Sample size determination, Technical analysis and Regression.
Many of his research projects under Statistics are closely connected to Geography, Metis and Distribution with Geography, Metis and Distribution, tying the diverse disciplines of science together.
In his work, Kernel regression, Correlation, Transformation and Additive model is strongly intertwined with Unit root, which is a subfield of Nonparametric statistics.
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