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Details about Badih Ghattas

Workplace:Groupement de Recherche en Économie Quantitative d'Aix-Marseille (GREQAM), École d'Économie d'Aix-Marseille (Aix-Marseille School of Economics (AMSE)), Aix-Marseille Université (Aix-Marseille University), (more information at EDIRC)

Access statistics for papers by Badih Ghattas.

Last updated 2023-06-19. Update your information in the RePEc Author Service.

Short-id: pgh239


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Working Papers

2023

  1. Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments
    Post-Print, HAL Downloads

2019

  1. Assessing variable importance in clustering: a new method based on unsupervised binary decision trees
    Post-Print, HAL Downloads
    See also Journal Article Assessing variable importance in clustering: a new method based on unsupervised binary decision trees, Computational Statistics, Springer (2019) Downloads (2019)

2016

  1. A Multidimensional Computerized Adaptive Short-Form Quality of Life ă Questionnaire Developed and Validated for Multiple Sclerosis The ă MusiQoL-MCAT
    Post-Print, HAL View citations (1)

2000

  1. Importance des variables dans les methodes CART
    G.R.E.Q.A.M., Universite Aix-Marseille III

1999

  1. Agregation d'arbres de classification
    G.R.E.Q.A.M., Universite Aix-Marseille III
  2. Previsions des pics d'ozone par arbres de regression, simples et agreges par bootstrap
    G.R.E.Q.A.M., Universite Aix-Marseille III
  3. Previsions par arbres de classification
    G.R.E.Q.A.M., Universite Aix-Marseille III

Journal Articles

2023

  1. Machine Learning Alternatives to Response Surface Models
    Mathematics, 2023, 11, (15), 1-27 Downloads View citations (1)

2022

  1. Improved linear regression prediction by transfer learning
    Computational Statistics & Data Analysis, 2022, 174, (C) Downloads View citations (1)

2021

  1. A combined strategy for multivariate density estimation
    Journal of Nonparametric Statistics, 2021, 33, (1), 39-59 Downloads

2019

  1. Assessing variable importance in clustering: a new method based on unsupervised binary decision trees
    Computational Statistics, 2019, 34, (1), 301-321 Downloads
    See also Working Paper Assessing variable importance in clustering: a new method based on unsupervised binary decision trees, Post-Print (2019) Downloads (2019)
  2. Multivariate and functional robust fusion methods for structured Big Data
    Journal of Multivariate Analysis, 2019, 170, (C), 149-161 Downloads View citations (1)

2017

  1. Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events
    Ecological Modelling, 2017, 360, (C), 80-93 Downloads

2015

  1. Exploring the Response Shift Effect on the Quality of Life of Patients with Schizophrenia
    Medical Decision Making, 2015, 35, (3), 388-397 Downloads

2013

  1. Interpretable clustering using unsupervised binary trees
    Advances in Data Analysis and Classification, 2013, 7, (2), 125-145 Downloads View citations (5)
  2. Nonparametric comparison of several transformations of distribution functions
    Journal of Nonparametric Statistics, 2013, 25, (3), 619-633 Downloads

2012

  1. A review of supervised machine learning algorithms and their applications to ecological data
    Ecological Modelling, 2012, 240, (C), 113-122 Downloads View citations (10)

2007

  1. Classifying densities using functional regression trees: Applications in oceanology
    Computational Statistics & Data Analysis, 2007, 51, (10), 4984-4993 Downloads View citations (23)
 
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