• Home
  • Historical notes
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
    • Sources of Financing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • Article Processing Charges
    • License Agreement
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Contacts
en English
  • Українська Українська

UkrainianProfessional Education

  • Submit an article
  • Home
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
    • Sources of Financing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • Article Processing Charges
    • License Agreement
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Search
  • Contacts

Article

  • Read article
  • Download article

Received 16.12.2022

Revised 15.05.2023

Accepted 14.06.2023

Retrieved from Iss. 113, P. 2, 2023

Pages 117 -132

  • 127 Views

Suggested citation

Artemenko, V., & Petrovych, V. (2023). NEW CONCEPT OF CHEMOMETRICS. Automobile Roads and Road Construction, (113.2), 117-132. https://doi.org/10.33744/0365-8171-2023-113.2-117-132

NEW CONCEPT OF CHEMOMETRICS

Vladyslav Artemenko Volodymyr Petrovych

Abstract

The parametric statistical methods of the research experimental data most often use presently in chemometrics. If experimental data do not correspond to the normal probabilistic distribution that in this case it is impossible prodice adequate data processing. At recently in chemometrics more often started to use the classical nonparametric methods. The classical nonparametric methods do not handle the raw datas probabilistic distribution. But these methods when undertakind the real calculations don’t care use that or other types of the distributions. The classical nonparametric methods under its realization usually requare referencing to corresponding statistical tables. Its own table is used for each such method. Chemometrics to presently presents itself many not bound between itself statistical methods of the investigations. The huge defect of the classical methods of chemometrics (parametric and nonparametric) in absence of the united approach to processing the raw datas. In this article is offered in fundamentally new approach to statistical investigations of the datas. If use the method of nonparametric bootstrap that possible replace many unbound between itself methods of classical chemometrics whole one ore two universal methods. And these universal methods have not what or essential defect. With standpoint of the program realization this means presence one or two universal procedures for decision nearly all practical problems of chemometrics. One of the primary tasks of nonparametric bootstrap as follows problem of the duplication of the sample is considered in article. Herewith simulated that was organized not one series of experiment but well over (for example 1000 … 10000). On example is shown use the method of nonparametric bootstrap with finding confidential interval for average and median for ecological time series. On programming language MATLAB is brought code corresponding bootstrap procedure

Keywords:

chemometrics, new paradigm of chemometrics, nonparametric bootstrap, real chemometrics data, universal methods and procedures, MATLAB code

References

  1. Meier, P.C., & Zünd, R.E. (2000). Statistical methods in analytical chemistry (2nd ed.). New York: John Wiley & Sons.
  2. Gemperline, P. (Ed.). (2006). Practical guide to chemometrics (2nd ed.). Boca Raton: CRC Press.
  3. Tarasova, V.V. (2008). Environmental statistics. Kyiv, Ukraine: Center for Educational Literature.
  4. Miller, J.N., & Miller, J.C. (2010). Statistics and chemometrics for analytical chemistry (6th ed.). Harlow: Prentice Hall.
  5. Chernick, M.R., & LaBudde, R.A. (2011). An introduction to bootstrap methods with applications to R. Hoboken: John Wiley & Sons.
  6. Zieffler, A.S., Harring, J.R., & Long, J.D. (2011). Comparing groups: Randomization and bootstrap methods using R. Hoboken: John Wiley & Sons.
  7. Artemenko, V.A., & Petrovych, V.V. (2019). Modern statistical methods for processing environmental data. Automobile Roads and Road Construction, 105, 35-43.
Share
Facebook
Twitter
LinkedIn
Email
Telegram
Viber
WhatsApp

https://doi.org/10.33744/0365-8171-2023-113.2-117-132

Address
01010, Ukraine, Kyiv,
1, M. Omelianovycha-Pavlenka Str.


Email
ntu@arrcjournal.org

Main information
  • Aims and Scope
  • Indexing
  • Terms of Publication
  • Editorial Board
  • Publication Ethics
Additional information
  • Complaints Policy
  • Peer Review Process
  • Open Access Policy
  • Anti-plagiarism Policy
  • Generative AI Policy
  • Archiving