What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
His main research concerns Applied mathematics, Statistics, Estimator, Nonparametric regression and Mathematical optimization.
His Applied mathematics research is multidisciplinary, relying on both Regression analysis, Linear regression, Polynomial regression and Polynomial.
His research in Estimator intersects with topics in Quantile, Kernel method and Conditional probability distribution.
The various areas that Jianqing Fan examines in his Nonparametric regression study include Generalized linear model, M-estimator and Local regression.
His work carried out in the field of Mathematical optimization brings together such families of science as Smoothing and Lasso.
His Penalty method research is multidisciplinary, incorporating perspectives in Likelihood function, Feature selection and Model selection.
His most cited work include:
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties (5871 citations)
- Local polynomial modelling and its applications (2773 citations)
- Sure independence screening for ultrahigh dimensional feature space (1695 citations)
What are the main themes of his work throughout his whole career to date?
Jianqing Fan mostly deals with Applied mathematics, Estimator, Statistics, Econometrics and Nonparametric statistics.
The concepts of his Applied mathematics study are interwoven with issues in Linear regression, Covariance, Linear model, Covariance matrix and Mathematical optimization.
Jianqing Fan has included themes like Smoothing and Polynomial regression in his Mathematical optimization study.
His Estimator research integrates issues from Algorithm, Sample size determination and Minimax.
His work on Volatility and Factor analysis is typically connected to Estimation as part of general Econometrics study, connecting several disciplines of science.
Jianqing Fan studies Nonparametric regression, a branch of Nonparametric statistics.
He most often published in these fields:
- Applied mathematics (32.71%)
- Estimator (30.26%)
- Statistics (25.94%)
What were the highlights of his more recent work (between 2018-2021)?
- Applied mathematics (32.71%)
- Estimator (30.26%)
- Factor analysis (7.33%)
In recent papers he was focusing on the following fields of study:
Jianqing Fan mainly focuses on Applied mathematics, Estimator, Factor analysis, Algorithm and Covariance.
His Applied mathematics study incorporates themes from Random matrix, Linear regression, Statistical inference, Minimax and Covariance matrix.
His Linear regression research is multidisciplinary, incorporating elements of Covariate, Sample size determination, Regression and Lasso.
His work in the fields of Estimation of covariance matrices and Robust statistics overlaps with other areas such as Thresholding and Initialization.
His Factor analysis research incorporates elements of Principal component analysis, Inference and Artificial intelligence.
His biological study spans a wide range of topics, including Statistical hypothesis testing, Regression analysis and Model selection.
Between 2018 and 2021, his most popular works were:
- Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval. (101 citations)
- A Theoretical Analysis of Deep Q-Learning (81 citations)
- Adaptive Huber Regression. (52 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
Applied mathematics, Estimator, Matrix completion, Algorithm and Random matrix are his primary areas of study.
His Applied mathematics research includes themes of Covariate, Covariance matrix, Upper and lower bounds, Symmetric matrix and Factor analysis.
In the subject of general Covariance matrix, his work in Estimation of covariance matrices is often linked to Thresholding, thereby combining diverse domains of study.
His study in Algorithm is interdisciplinary in nature, drawing from both Statistical inference, Null hypothesis and Covariance.
His Principal component analysis research focuses on Random projection and how it connects with Statistical model.
He has researched Bounded function in several fields, including Critical point, Condition number and Mathematical optimization.
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