Locally and globally robust Penalized Trimmed Squares regression

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Avramidis, A., and G. Zioutas. “Locally and Globally Robust Penalized Trimmed Squares Regression”. Simulation Modelling Practice and Theory, vol. 19, no. 1, 2011, pp. 148-60, https://doi.org/10.1016/j.simpat.2010.06.001.
Avramidis, A., & Zioutas, G. (2011). Locally and globally robust Penalized Trimmed Squares regression. Simulation Modelling Practice and Theory, 19(1), 148-160. https://doi.org/10.1016/j.simpat.2010.06.001
Avramidis, A., and G. Zioutas. “Locally and Globally Robust Penalized Trimmed Squares Regression”. Simulation Modelling Practice and Theory 19, no. 1 (2011): 148-60. https://doi.org/10.1016/j.simpat.2010.06.001.
Avramidis A, Zioutas G. Locally and globally robust Penalized Trimmed Squares regression. Simulation Modelling Practice and Theory. 2011;19(1):148-60.
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Refrences Analysis
Category Category Repetition
Science: Mathematics: Probabilities. Mathematical statistics13
Science: Mathematics13
Social Sciences: Economic theory. Demography: Economics as a science2
Social Sciences: Statistics1
Social Sciences: Commerce: Business1
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Refrences used by this article by year
Citations
Title Journal Journal Categories Citations Publication Date
A trimmed moving total least-squares method for curve and surface fitting Measurement Science and Technology
  • Technology: Engineering (General). Civil engineering (General)
  • Science: Mathematics: Instruments and machines
  • Science: Chemistry: Analytical chemistry
  • Technology: Engineering (General). Civil engineering (General)
 11 2020
A robust regression based on weighted LSSVM and penalized trimmed squares Chaos, Solitons & Fractals
  • Science: Mathematics
  • Science: Physics
  • Science: Mathematics
  • Science: Physics
 10 2016
A robust weighted least squares support vector regression based on least trimmed squares Neurocomputing
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
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 42 2015
Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
Technology: Mechanical engineering and machinery1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics1
Technology: Engineering (General). Civil engineering (General)1
Science: Mathematics: Instruments and machines1
Science: Chemistry: Analytical chemistry1
Science: Mathematics1
Science: Physics1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A robust weighted least squares support vector regression based on least trimmed squares and was published in 2015. The most recent citation comes from a 2020 study titled A trimmed moving total least-squares method for curve and surface fitting. This article reached its peak citation in 2020, with 1 citations. It has been cited in 3 different journals. Among related journals, the Measurement Science and Technology 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