Histogram Filter with Adjustment of the Smoothing Parameter Based on The Minimization of the Chi-Square Test

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Ausiannikau Andrei V
Ausiannikau Andrei V

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Histogram Filter with Adjustment of the Smoothing Parameter Based on  The Minimization of the Chi-Square Test

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Abstract

For the formation of adequate models of objects of statistical research, with the possible high cost of a measuring experiment or the process of obtaining data, fast and “correct” identification (recognition) of the probability distribution density (PDD) based on the construction of simple histogram estimates is required. The requirement for rapid identification can be considered equivalent to having a limited and small amount of data. The article proposes a theoretically substantiated method for constructing a histogram filter (HF), which is a linear combination of the amount of data in adjacent intervals with constant weight coefficients, which can be expressed in terms of a single coefficient k -the smoothing parameter. The estimation of the smoothing coefficient is based on the minimization of the modified chi-square test. The theorem given in the article establishes that the value of the mathematical expectation of the chisquare test, after applying the HF, decreases by k times compared to the standard mathematical expectation of the criterion with a unit inclusion function.

References

11 Cites in Article
  1. (null). Список литературы.
  2. Ю Орлов,Н (2013). Оптимальное разбиение гистограммы для оценивания выборочной плотности функции распределения нестационарного временного ряда, Препринты ИПМ им.
  3. Chong Gu,Yongho Jeon,Yi Lin (2013). Nonparametric density estimation in high-dimensions.
  4. L Devroye,L Gyorfi (1985). Nonparametric Density Estimation: The L1 View.
  5. Chris Solomon,Toby Breckon (2010). Fundamentals of Digital Image Processing.
  6. Gonzalez Rafael (2018). Digital image processing.
  7. Андрей Овсянников,Олег Барашко (2021). Гистограммный фильтр на основе нечеткой принадлежности данных интервалу группирования.
  8. А Овсянников (2009). Статистические неравенства в сверхрегулярных статистических экспериментах теории оценивания // ВестiнацыянальнайакадэмiiнавукБеларусi.
  9. П Новицкий,И Зограф,Оценка (1991). погрешностей результатов наблюдений.
  10. D Freedman,P Diaconis (1981). On the histogram as a density estimatorr: L2 theory // Zeitschrift fur Wahrscheinlichkeitstheorie verw.
  11. David Scott (1979). On optimal and data-based histograms.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ausiannikau Andrei V. 2026. \u201cHistogram Filter with Adjustment of the Smoothing Parameter Based on The Minimization of the Chi-Square Test\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 22 (GJRE Volume 22 Issue J3): .

Download Citation

Enhanced algorithm for histogram smoothing in engineering studies.
Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords
Classification
GJRE-J Classification: DDC Code: 020.3 LCC Code: Z1006
Version of record

v1.2

Issue date

September 12, 2022

Language
ru
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Published Article

For the formation of adequate models of objects of statistical research, with the possible high cost of a measuring experiment or the process of obtaining data, fast and “correct” identification (recognition) of the probability distribution density (PDD) based on the construction of simple histogram estimates is required. The requirement for rapid identification can be considered equivalent to having a limited and small amount of data. The article proposes a theoretically substantiated method for constructing a histogram filter (HF), which is a linear combination of the amount of data in adjacent intervals with constant weight coefficients, which can be expressed in terms of a single coefficient k -the smoothing parameter. The estimation of the smoothing coefficient is based on the minimization of the modified chi-square test. The theorem given in the article establishes that the value of the mathematical expectation of the chisquare test, after applying the HF, decreases by k times compared to the standard mathematical expectation of the criterion with a unit inclusion function.

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Histogram Filter with Adjustment of the Smoothing Parameter Based on The Minimization of the Chi-Square Test

Ausiannikau Andrei V
Ausiannikau Andrei V

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