What is he best known for?
The fields of study he is best known for:
- Statistics
- Regression analysis
- Machine learning
His primary areas of study are Nonparametric statistics, Functional data analysis, Statistics, Nonparametric regression and Econometrics.
His studies deal with areas such as Industrial engineering, Random variable, Functional regression, Rate of convergence and Operations research as well as Nonparametric statistics.
His study looks at the intersection of Functional data analysis and topics like Artificial intelligence with Machine learning.
His Kernel density estimation study in the realm of Statistics interacts with subjects such as Development, Scope, Field and Set.
He has included themes like Smoothing, Mathematical optimization and Kernel in his Nonparametric regression study.
His Econometrics research includes themes of Regular conditional probability, Linear model and Asymptotic distribution.
His most cited work include:
- Nonparametric functional data analysis : theory and practice (929 citations)
- Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics) (518 citations)
- Nonparametric Curve Estimation from Time Series (303 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Nonparametric statistics, Statistics, Functional data analysis, Applied mathematics and Nonparametric regression.
His biological study spans a wide range of topics, including Additive model, Mathematical optimization, Artificial intelligence and Pattern recognition.
His Regression analysis, Multivariate statistics, Linear regression and Consistency study in the realm of Statistics connects with subjects such as Estimation.
He works mostly in the field of Functional data analysis, limiting it down to topics relating to Conditional probability distribution and, in certain cases, Regular conditional probability and Random variable, as a part of the same area of interest.
His Applied mathematics research is multidisciplinary, relying on both Parametric statistics, Estimator, Density estimation, Linear model and Dimensionality reduction.
His Nonparametric regression research focuses on Covariate and how it relates to Boosting.
He most often published in these fields:
- Nonparametric statistics (32.69%)
- Statistics (31.41%)
- Functional data analysis (26.28%)
What were the highlights of his more recent work (between 2017-2021)?
- Functional data analysis (26.28%)
- Dimensionality reduction (8.33%)
- Applied mathematics (23.72%)
In recent papers he was focusing on the following fields of study:
Philippe Vieu mostly deals with Functional data analysis, Dimensionality reduction, Applied mathematics, Estimator and Statistics.
His work deals with themes such as Data mining, Kernel, Covariate, Algorithm and Data science, which intersect with Functional data analysis.
His study in Data mining is interdisciplinary in nature, drawing from both Nonparametric statistics, Multivariate analysis, Field and High-dimensional statistics.
His Applied mathematics course of study focuses on Asymptotic distribution and Missing data.
His Estimator research is multidisciplinary, incorporating elements of Rate of convergence, Test statistic and Sample size determination.
In general Statistics study, his work on Nonparametric regression often relates to the realm of Estimation, thereby connecting several areas of interest.
Between 2017 and 2021, his most popular works were:
- Recent advances in functional data analysis and high-dimensional statistics (33 citations)
- Nonparametric modelling for functional data: selected survey and tracks for future (32 citations)
- Kernel Regression Estimation for Functional Data (21 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Regression analysis
- Machine learning
Functional data analysis, Data science, Statistics, Kernel regression and Dimensionality reduction are his primary areas of study.
His Functional data analysis study integrates concerns from other disciplines, such as Nonparametric statistics, Kernel, Regression, Algorithm and Sample.
His Statistics research includes elements of Kernel and Combinatorics.
Many of his studies on Kernel regression apply to Feature as well.
His Dimensionality reduction study incorporates themes from High dimensional, Theoretical computer science, Sparse regression and Big data.
His Estimation investigation overlaps with other areas such as Pattern recognition, Variable, Regression problems, Artificial intelligence and Linear model.
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