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Mathematics

D-Index
33
Citations
9154
World Ranking
2982
National Ranking
174

Overview

Frédéric Ferraty is affiliated with the University of Toulouse-Jean Jaurès in France. Their research spans several interconnected fields, including Mathematics, Agricultural and Biological Sciences, and Computer Science.

The scientist's work includes a focus on the subfields of Statistics and Probability, Artificial Intelligence, Insect Science, Ecology, Evolution, Behavior and Systematics, and Biomedical Engineering. This diverse expertise is reflected in the range of topics they address, such as Statistical Methods and Inference, Bayesian Methods and Mixture Models, Advanced Statistical Methods and Models, Forensic Entomology and Diptera Studies, Insect behavior and control techniques, Entomological Studies and Ecology, and Neural Networks and Applications.

Recent publications by Frédéric Ferraty demonstrate an engagement with applied statistics and functional data analysis. Notable papers include:

  • "Estimation of temperature-dependent growth profiles for the assessment of time of hatching in forensic entomology," 2023, Journal of the Royal Statistical Society Series C (Applied Statistics)
  • "Scalar-on-function local linear regression and beyond," 2021, Biometrika
  • "2nd Special issue on Functional Data Analysis," 2021, Econometrics and Statistics
  • "Editorial for the 2nd special issue on high-dimensional and functional data analysis," 2023, Computational Statistics & Data Analysis

Frequent collaborators include:

  • Davide Pigoli
  • John A. D. Aston
  • Anjali Mazumder
  • C. S. Richards
  • M. J. R. Hall

Frédéric Ferraty's work has been published in a variety of journals with specialized focus areas, reflecting the breadth of their research interests and interdisciplinary approach. These venues include the Journal of the Royal Statistical Society Series C (Applied Statistics), Biometrika, Econometrics and Statistics, and Computational Statistics & Data Analysis.

Best Publications

  • Nonparametric functional data analysis : theory and practice

    Frédéric Ferraty;Philippe Vieu

  • Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)

    Frédéric Ferraty;Philippe Vieu

  • Functional linear model

    Hervé Cardot;Frédéric Ferraty;Pascal Sarda

  • Curves discrimination: a nonparametric functional approach

    Frédéric Ferraty;Frédéric Ferraty;Philippe Vieu

  • The Functional Nonparametric Model and Application to Spectrometric Data

    Frédéric Ferraty;Philippe Vieu

  • The Oxford Handbook of Functional Data Analysis

    Frédéric Ferraty;Y Romain

  • Nonparametric regression on functional data: inference and practical aspects

    Frédéric Ferraty;André Mas;Philippe Vieu

  • Nonparametric models for functional data, with application in regression, time series prediction and curve discrimination

    F. Ferraty;P. Vieu

  • Testing hypotheses in the functional linear model

    Hervé Cardot;Frédéric Ferraty;André Mas;Pascal Sarda

  • Rate of uniform consistency for nonparametric estimates with functional variables

    Frédéric Ferraty;Ali Laksaci;Amel Tadj;Philippe Vieu

  • k-Nearest Neighbour method in functional nonparametric regression

    Florent Burba;Frédéric Ferraty;Philippe Vieu

  • Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models

    Frédéric Ferraty;Ali Laksaci;Philippe Vieu

  • Conditional Quantiles for Dependent Functional Data with Application to the Climatic El Niño Phenomenon

    Frédéric Ferraty;Algeria Philippe Vieu

  • Locally modelled regression and functional data

    J. Barrientos-Marin;Frédéric Ferraty;Philippe Vieu

  • Cross-validated estimations in the single-functional index model

    Ahmed Ait-Saïdi;Frédéric Ferraty;Rabah Kassa;Philippe Vieu

  • Simultaneous non-parametric regressions of unbalanced longitudinal data

    Philippe C. Besse;Hervé Cardot;Frédéric Ferraty

  • Local smoothing regression with functional data

    K. Benhenni;F. Ferraty;M. Rachdi;P. Vieu

  • Regression when both response and predictor are functions

    F. Ferraty;I. Van Keilegom;P. Vieu

  • Additive prediction and boosting for functional data

    Frédéric Ferraty;Philippe Vieu

  • Most-predictive design points for functional data predictors

    Frédéric Ferraty;P. Hall;Philippe Vieu

Frequent Co-Authors

Philippe Vieu
Philippe Vieu Paul Sabatier University
Piotr Kokoszka
Piotr Kokoszka Colorado State University

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