Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي
  • المؤلفون: Lim JS;Lim JS;Lim JS; Kim YD; Kim YD; Woo JC; Woo JC
  • المصدر:
    Molecules (Basel, Switzerland) [Molecules] 2024 Mar 11; Vol. 29 (6). Date of Electronic Publication: 2024 Mar 11.
  • نوع النشر :
    Journal Article
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 100964009 Publication Model: Electronic Cited Medium: Internet ISSN: 1420-3049 (Electronic) Linking ISSN: 14203049 NLM ISO Abbreviation: Molecules Subsets: PubMed not MEDLINE; MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, c1995-
    • نبذة مختصرة :
      For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance-covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables.
    • References:
      Analyst. 2010 Aug;135(8):1961-9. (PMID: 20577693)
      Anal Chem. 2023 Feb 28;95(8):3917-3921. (PMID: 36786555)
      Anal Chem. 2022 Nov 22;94(46):15997-16005. (PMID: 36343110)
      J Chromatogr A. 1997 Feb 21;762(1-2):73-82. (PMID: 9098967)
      Analyst. 2005 Mar;130(3):370-8. (PMID: 15724167)
      Anal Chem. 2019 Jul 16;91(14):8715-8722. (PMID: 31180654)
      Anal Chem. 1996 Jun 1;68(11):1851-7. (PMID: 21619096)
      Analyst. 2007 Jun;132(6):536-43. (PMID: 17525810)
      Anal Chem. 2020 Aug 18;92(16):10863-10871. (PMID: 32678579)
    • Grant Information:
      RS-2023-00265582 Ministry of Trade, Industry & Energy; 23011081 Korea Research Institute of Standards and Science
    • Contributed Indexing:
      Keywords: Monte Carlo simulation; calibration; correlated independent variables; ordinary least squares; uncertainty evaluation
    • الموضوع:
      Date Created: 20240328 Latest Revision: 20240426
    • الموضوع:
      20240426
    • الرقم المعرف:
      PMC10975691
    • الرقم المعرف:
      10.3390/molecules29061248
    • الرقم المعرف:
      38542885