Regression model for surrogate data in high dimensional statistics

Article Properties
  • Language
    English
  • DOI (url)
    10.1080/03610926.2019.1586940
  • Publication Date
    2019/07/11
  • Journal
    Communications in Statistics - Theory and Methods
  • Indian UGC (journal)
  • Refrences
    47
  • Citations
    4
  • Firas Ibrahim Faculté de Médecine, Univ. Grenoble Alpes, AGEIS EA 7407 laboratory, La Tronche, France;
  • Ali Hajj Hassan Univ. Grenoble Alpes, CNRS, LJK, Saint-Martin-d’Hères, France;
  • Jacques Demongeot Faculté de Médecine, Univ. Grenoble Alpes, AGEIS EA 7407 laboratory, La Tronche, France;Univ. Grenoble Alpes, AGEIS Laboratory, Grenoble Cedex, France ORCID (unauthenticated)
  • Mustapha Rachdi Univ. Grenoble Alpes, AGEIS Laboratory, Grenoble Cedex, France ORCID (unauthenticated)
Cite
Ibrahim, Firas, et al. “Regression Model for Surrogate Data in High Dimensional Statistics”. Communications in Statistics - Theory and Methods, vol. 49, no. 13, 2019, pp. 3206-27, https://doi.org/10.1080/03610926.2019.1586940.
Ibrahim, F., Hajj Hassan, A., Demongeot, J., & Rachdi, M. (2019). Regression model for surrogate data in high dimensional statistics. Communications in Statistics - Theory and Methods, 49(13), 3206-3227. https://doi.org/10.1080/03610926.2019.1586940
Ibrahim, Firas, Ali Hajj Hassan, Jacques Demongeot, and Mustapha Rachdi. “Regression Model for Surrogate Data in High Dimensional Statistics”. Communications in Statistics - Theory and Methods 49, no. 13 (2019): 3206-27. https://doi.org/10.1080/03610926.2019.1586940.
1.
Ibrahim F, Hajj Hassan A, Demongeot J, Rachdi M. Regression model for surrogate data in high dimensional statistics. Communications in Statistics - Theory and Methods. 2019;49(13):3206-27.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Citations
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Estimation in nonparametric functional-on-functional models with surrogate responses Journal of Multivariate Analysis
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k‐Nearest neighbors local linear regression for functional and missing data at random

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Citations Analysis
Category Category Repetition
Science: Mathematics: Probabilities. Mathematical statistics3
Science: Mathematics3
Social Sciences: Commerce: Business: Accounting. Bookkeeping1
Social Sciences: Finance1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
The category Science: Mathematics: Probabilities. Mathematical statistics 3 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled k‐Nearest neighbors local linear regression for functional and missing data at random and was published in 2020. The most recent citation comes from a 2023 study titled Estimation in nonparametric functional-on-functional models with surrogate responses. This article reached its peak citation in 2023, with 2 citations. It has been cited in 4 different journals. Among related journals, the Journal of Multivariate Analysis cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year