What is she best known for?
The fields of study she is best known for:
- Statistics
- Regression analysis
- Random variable
Nonparametric regression, Applied mathematics, Mathematical optimization, Estimator and Statistics are her primary areas of study.
In her work, Multivariate statistics and Series is strongly intertwined with Polynomial regression, which is a subfield of Nonparametric regression.
Her Applied mathematics study combines topics from a wide range of disciplines, such as Polynomial and Log-linear model.
Her study in Polynomial is interdisciplinary in nature, drawing from both Model complexity, Applied probability and Backfitting algorithm.
Irène Gijbels has researched Estimator in several fields, including Copula and Kernel method.
Her Statistics research includes themes of Linear combination and Econometrics.
Her most cited work include:
- Local polynomial modelling and its applications (2773 citations)
- Local Polynomial Modeling and Its Applications (790 citations)
- Generalized Partially Linear Single-Index Models (636 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Estimator, Applied mathematics, Statistics, Econometrics and Mathematical optimization.
Her Estimator research incorporates elements of Nonparametric statistics and Random variable.
Her research integrates issues of Conditional expectation, Polynomial, Kernel and Consistency in her study of Applied mathematics.
Her Polynomial study often links to related topics such as Polynomial regression.
The concepts of her Mathematical optimization study are interwoven with issues in Feature selection, Linear regression, Nonparametric regression and Local regression.
Her Nonparametric regression study also includes fields such as
- Smoothing, which have a strong connection to Classification of discontinuities,
- Wavelet and related Algorithm.
She most often published in these fields:
- Estimator (38.41%)
- Applied mathematics (35.37%)
- Statistics (34.76%)
What were the highlights of her more recent work (between 2013-2021)?
- Econometrics (25.00%)
- Quantile (11.59%)
- Statistics (34.76%)
In recent papers she was focusing on the following fields of study:
Irène Gijbels focuses on Econometrics, Quantile, Statistics, Estimator and Applied mathematics.
Her work on Covariate, Pareto principle and Heavy-tailed distribution as part of general Statistics study is frequently linked to Pareto interpolation, bridging the gap between disciplines.
Her Estimator research is multidisciplinary, relying on both Smoothing, Nonparametric statistics, Linear regression, Pareto distribution and Feature selection.
Her Applied mathematics research is multidisciplinary, incorporating perspectives in Maximum likelihood, Method of moments, Polynomial and Inference.
Her biological study spans a wide range of topics, including Series and System dynamics.
Her Mathematical optimization study incorporates themes from Nonparametric regression and Consistency.
Between 2013 and 2021, her most popular works were:
- Estimation of a Copula when a Covariate Affects only Marginal Distributions (24 citations)
- P-splines quantile regression estimation in varying coefficient models (20 citations)
- Integrated depth for functional data: statistical properties and consistency (19 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Random variable
- Regression analysis
Irène Gijbels mostly deals with Econometrics, Statistics, Estimator, Mathematical optimization and Random variable.
In her study, Conditional expectation, Expected shortfall, Multivariate random variable and Applied mathematics is inextricably linked to Quantile function, which falls within the broad field of Econometrics.
As part of the same scientific family, Irène Gijbels usually focuses on Estimator, concentrating on Nonparametric statistics and intersecting with Density estimation, Covariate and Parametric statistics.
Her work carried out in the field of Mathematical optimization brings together such families of science as Linear regression, Multivariate adaptive regression splines, Polynomial regression, Segmented regression and Regression analysis.
Her Regression analysis research integrates issues from Feature selection and Outlier.
Her work in the fields of Random variable, such as Marginal distribution, intersects with other areas such as Weak convergence.
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