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Badih Ghattas

Personal Details

First Name:Badih
Middle Name:
Last Name:Ghattas
Suffix:
RePEc Short-ID:pgh239
[This author has chosen not to make the email address public]

Affiliation

Groupement de Recherche en Économie Quantitative d'Aix-Marseille (GREQAM)
École d'Économie d'Aix-Marseille
Aix-Marseille Université

Marseille, France
http://www.greqam.fr/
RePEc:edi:greqafr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Diane Manzon & Badih Ghattas & Magalie Claeys-Bruno & Sophie Declomesnil & Christophe Carité & Michelle Sergent, 2023. "Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments," Post-Print hal-04021786, HAL.
  2. Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Post-Print hal-02007388, HAL.
  3. Pierre Michel & Karine Baumstarck & Badih Ghattas & Jean Pelletier & Anderson Loundou & Mohamed Boucekine & Pascal Auquier & Laurent Boyer, 2016. "A Multidimensional Computerized Adaptive Short-Form Quality of Life ă Questionnaire Developed and Validated for Multiple Sclerosis The ă MusiQoL-MCAT," Post-Print hal-01482540, HAL.
  4. Ghattas, B., 2000. "Importance des variables dans les methodes CART," G.R.E.Q.A.M. 00b04, Universite Aix-Marseille III.
  5. Ghattas, B., 1999. "Previsions des pics d'ozone par arbres de regression, simples et agreges par bootstrap," G.R.E.Q.A.M. 99b04, Universite Aix-Marseille III.
  6. Ghattas, B., 1999. "Previsions par arbres de classification," G.R.E.Q.A.M. 99b03, Universite Aix-Marseille III.
  7. Ghattas, B., 1999. "Agregation d'arbres de classification," G.R.E.Q.A.M. 99b05, Universite Aix-Marseille III.

Articles

  1. Badih Ghattas & Diane Manzon, 2023. "Machine Learning Alternatives to Response Surface Models," Mathematics, MDPI, vol. 11(15), pages 1-27, August.
  2. Obst, David & Ghattas, Badih & Claudel, Sandra & Cugliari, Jairo & Goude, Yannig & Oppenheim, Georges, 2022. "Improved linear regression prediction by transfer learning," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  3. Alejandro Cholaquidis & Ricardo Fraiman & Badih Ghattas & Juan Kalemkerian, 2021. "A combined strategy for multivariate density estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 33(1), pages 39-59, January.
  4. Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Computational Statistics, Springer, vol. 34(1), pages 301-321, March.
  5. Aaron, Catherine & Cholaquidis, Alejandro & Fraiman, Ricardo & Ghattas, Badih, 2019. "Multivariate and functional robust fusion methods for structured Big Data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 149-161.
  6. Crisci, Carolina & Terra, Rafael & Pacheco, Juan Pablo & Ghattas, Badih & Bidegain, Mario & Goyenola, Guillermo & Lagomarsino, Juan José & Méndez, Gustavo & Mazzeo, Néstor, 2017. "Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events," Ecological Modelling, Elsevier, vol. 360(C), pages 80-93.
  7. Mohamed Boucekine & Laurent Boyer & Karine Baumstarck & Aurelie Millier & Badih Ghattas & Pascal Auquier & Mondher Toumi, 2015. "Exploring the Response Shift Effect on the Quality of Life of Patients with Schizophrenia," Medical Decision Making, , vol. 35(3), pages 388-397, April.
  8. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.
  9. Mohamed Boutahar & Badih Ghattas & Denys Pommeret, 2013. "Nonparametric comparison of several transformations of distribution functions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 619-633, September.
  10. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
  11. Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Pierre Michel & Karine Baumstarck & Badih Ghattas & Jean Pelletier & Anderson Loundou & Mohamed Boucekine & Pascal Auquier & Laurent Boyer, 2016. "A Multidimensional Computerized Adaptive Short-Form Quality of Life ă Questionnaire Developed and Validated for Multiple Sclerosis The ă MusiQoL-MCAT," Post-Print hal-01482540, HAL.

    Cited by:

    1. Chun Wang & David J. Weiss & Zhuoran Shang, 2019. "Variable-Length Stopping Rules for Multidimensional Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 749-771, September.

Articles

  1. Badih Ghattas & Diane Manzon, 2023. "Machine Learning Alternatives to Response Surface Models," Mathematics, MDPI, vol. 11(15), pages 1-27, August.

    Cited by:

    1. Yanlin Wang & Zhijun Cheng & Zichen Wang, 2024. "Multi-Output Bayesian Support Vector Regression Considering Dependent Outputs," Mathematics, MDPI, vol. 12(18), pages 1-20, September.

  2. Obst, David & Ghattas, Badih & Claudel, Sandra & Cugliari, Jairo & Goude, Yannig & Oppenheim, Georges, 2022. "Improved linear regression prediction by transfer learning," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

    Cited by:

    1. Antoniadis, Anestis & Gaucher, Solenne & Goude, Yannig, 2024. "Hierarchical transfer learning with applications to electricity load forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 641-660.

  3. Aaron, Catherine & Cholaquidis, Alejandro & Fraiman, Ricardo & Ghattas, Badih, 2019. "Multivariate and functional robust fusion methods for structured Big Data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 149-161.

    Cited by:

    1. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.

  4. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.

    Cited by:

    1. Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Post-Print hal-02007388, HAL.
    2. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    3. Adriano Zanin Zambom & Julian A. A. Collazos & Ronaldo Dias, 2019. "Functional data clustering via hypothesis testing k-means," Computational Statistics, Springer, vol. 34(2), pages 527-549, June.
    4. Jan Pablo Burgard & Carina Moreira Costa & Martin Schmidt, 2024. "Robustification of the k-means clustering problem and tailored decomposition methods: when more conservative means more accurate," Annals of Operations Research, Springer, vol. 339(3), pages 1525-1568, August.
    5. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).

  5. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.

    Cited by:

    1. Olatunji, Obafemi O. & Akinlabi, Stephen & Madushele, Nkosinathi & Adedeji, Paul A., 2020. "Property-based biomass feedstock grading using k-Nearest Neighbour technique," Energy, Elsevier, vol. 190(C).
    2. Crisci, Carolina & Terra, Rafael & Pacheco, Juan Pablo & Ghattas, Badih & Bidegain, Mario & Goyenola, Guillermo & Lagomarsino, Juan José & Méndez, Gustavo & Mazzeo, Néstor, 2017. "Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events," Ecological Modelling, Elsevier, vol. 360(C), pages 80-93.
    3. Zonlehoua Coulibali & Athyna Nancy Cambouris & Serge-Étienne Parent, 2020. "Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-32, August.
    4. Beáta Novotná & Ľuboš Jurík & Ján Čimo & Jozef Palkovič & Branislav Chvíla & Vladimír Kišš, 2022. "Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    5. Muñoz-Mas, R. & Martínez-Capel, F. & Alcaraz-Hernández, J.D. & Mouton, A.M., 2015. "Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?," Ecological Modelling, Elsevier, vol. 309, pages 72-81.
    6. Simidjievski, Nikola & Todorovski, Ljupčo & Džeroski, Sašo, 2015. "Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems," Ecological Modelling, Elsevier, vol. 306(C), pages 305-317.
    7. Alison Pereira Ribeiro & Nádia Felix Felipe da Silva & Fernanda Neiva Mesquita & Priscila de Cássia Souza Araújo & Thierson Couto Rosa & José Neiva Mesquita-Neto, 2021. "Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-21, September.
    8. Shen, Jian & Qin, Qubin & Wang, Ya & Sisson, Mac, 2019. "A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading," Ecological Modelling, Elsevier, vol. 398(C), pages 44-54.
    9. Yeeun Shin & Suyeon Kim & Se-Rin Park & Taewoo Yi & Chulgoo Kim & Sang-Woo Lee & Kyungjin An, 2022. "Identifying Key Environmental Factors for Paulownia coreana Habitats: Implementing National On-Site Survey and Machine Learning Algorithms," Land, MDPI, vol. 11(4), pages 1-16, April.
    10. Hua Shi & George Xian & Roger Auch & Kevin Gallo & Qiang Zhou, 2021. "Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology," Land, MDPI, vol. 10(8), pages 1-30, August.

  6. Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.

    Cited by:

    1. Park, Juhyun & Gasser, Theo & Rousson, Valentin, 2009. "Structural components in functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3452-3465, July.
    2. Crawford, F. & Watling, D.P. & Connors, R.D., 2017. "A statistical method for estimating predictable differences between daily traffic flow profiles," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 196-213.
    3. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.
    4. van der Linde, Angelika, 2008. "Variational Bayesian functional PCA," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 517-533, December.
    5. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    6. Alonso, Andrés M. & Casado, David & Romo, Juan, 2012. "Supervised classification for functional data: A weighted distance approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2334-2346.
    7. Karel Hron & Jitka Machalová & Alessandra Menafoglio, 2023. "Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation," Statistical Papers, Springer, vol. 64(5), pages 1629-1667, October.
    8. Germán Aneiros-Pérez & Philippe Vieu, 2013. "Testing linearity in semi-parametric functional data analysis," Computational Statistics, Springer, vol. 28(2), pages 413-434, April.
    9. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
    10. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
    11. Maria Ruiz-Medina & Rosa Espejo & Elvira Romano, 2014. "Spatial functional normal mixed effect approach for curve classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 257-285, September.
    12. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.
    13. Zhang, Zhen & Müller, Hans-Georg, 2011. "Functional density synchronization," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2234-2249, July.
    14. Croux, Christophe & Joossens, Kristel & Lemmens, Aurelie, 2007. "Trimmed bagging," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 362-368, September.
    15. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
    16. Lane, Stephen E. & Robinson, Andrew P., 2011. "An alternative objective function for fitting regression trees to functional response variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2557-2567, September.
    17. Casado, David, 2009. "Classification of functional data: a weighted distance approach," DES - Working Papers. Statistics and Econometrics. WS ws093915, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Shu-Fu Kuo & Yu-Shan Shih, 2012. "Variable selection for functional density trees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1387-1395, December.
    19. Petersen, Alexander & Zhang, Chao & Kokoszka, Piotr, 2022. "Modeling Probability Density Functions as Data Objects," Econometrics and Statistics, Elsevier, vol. 21(C), pages 159-178.
    20. Delicado, P., 2011. "Dimensionality reduction when data are density functions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 401-420, January.
    21. Antonio D’Ambrosio & Willem J. Heiser, 2016. "A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 774-794, September.
    22. Elena Ballante & Marta Galvani & Pierpaolo Uberti & Silvia Figini, 2021. "Polarized Classification Tree Models: Theory and Computational Aspects," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 481-499, October.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (1) 2020-06-29
  2. NEP-DES: Economic Design (1) 2024-03-04
  3. NEP-ECM: Econometrics (1) 2020-06-29

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