Derivative domain fitting: A new method for resolving a mixture of normal distributions in the presence of a contaminating background

D. H. Moore, Joe Gray

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Derivative domain least squares analysis is a new method for resolving multiple peaks superimposed on a slowly varying continuum into separate normal (Gaussian) distributions without developing a functional approximation for the continuum. The method is based on fitting the first derivative of the data with the first derivative of the sum of a series of normal distributions. A functional approximation for the continuum is not necessary as long as the first derivative of the continuum is approximately zero (i.e., the continuum varies slowly compared to the normal distributions).

Original languageEnglish (US)
Pages (from-to)510-518
Number of pages9
JournalCytometry
Volume14
Issue number5
StatePublished - 1993
Externally publishedYes

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Normal Distribution
Least-Squares Analysis

Keywords

  • background continuum
  • bivariate distributions
  • chromosomes
  • flow karyotype
  • normal mixture decomposition

ASJC Scopus subject areas

  • Biophysics
  • Cell Biology
  • Endocrinology
  • Hematology
  • Pathology and Forensic Medicine

Cite this

Derivative domain fitting : A new method for resolving a mixture of normal distributions in the presence of a contaminating background. / Moore, D. H.; Gray, Joe.

In: Cytometry, Vol. 14, No. 5, 1993, p. 510-518.

Research output: Contribution to journalArticle

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