Journal article
Identifying unimodal finite mixture models in data sets using distribution shifting – A novel approach
Results in engineering, Vol.26, 105393
06/2025
DOI: 10.1016/j.rineng.2025.105393
Abstract
•There are tests for bimodal distributions.•There are no tests for identifying unimodal finite mixture distributions.•We present a method for identifying unimodal finite mixture distributions.•Identifying sub-distributions in finite mixture models can have significance.
Finite mixture models represent data distributions composed of several overlapping sub-distributions instead of a single distribution. However, there are few methods and statistical tests for examining the distribution of variables in experimental data to determine if they arise from single or multiple distributions. The unimodal finite mixture model is identified as a type of data set for which little if any statistical approach exists. The morphology (size and shape) of a particle or cell is often important in the analysis of physical or physiological processes, often expressed as a distribution. These variables may be represented by either a single distribution or several distributions represented by a finite mixture model. In this work, number and volume distributions used in morphological analysis are investigated relating to the question of unimodal single distributions compared to finite mixture models. We derived and generalized a mathematical relationship between the number and volume distribution. We show that applying a variate-dependent weight to a unimodal data distribution (number) should result in a shifted unimodal distribution (area or volume). However, if the number data distribution is represented by more than one distribution, the transformed function can result in deformation of the volume distribution and possibly the appearance of additional modes as compared to a single distribution. We developed a novel method that can aid in identifying data distributions which may be unimodal finite mixture models. We applied this approach to simulated data and data sets from two published experiments to demonstrate its use with real data.
Details
- Title: Subtitle
- Identifying unimodal finite mixture models in data sets using distribution shifting – A novel approach
- Creators
- Paul Allen Williams - Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa, USAEmily K. Roberts - Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USAQuynh-Anh Nguyen - University of IndianapolisKamal Rahmouni - University of Iowa, Iowa Neuroscience Institute
- Resource Type
- Journal article
- Publication Details
- Results in engineering, Vol.26, 105393
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.rineng.2025.105393
- ISSN
- 2590-1230
- eISSN
- 2590-1230
- Grant note
- National Institutes of Health: DK007690, DK112751, HL162773, HL172944 Veterans Affairs: BX004249, BX006040 American Heart Association Postdoctoral Fellowship: 834962 University of Iowa Fraternal Order of Eagles Diabetes Research Center
The authors greatly appreciate and thank Ian C. Clarke of Loma Linda University in California, for his support and encouragement in this project. The authors also acknowledge Christopher G. Wilson at Loma Linda University in California for his support and assistance in the writing of the manuscript. This work was partially supported by National Institutes of Health (DK007690, DK112751, HL162773, and HL172944) , Veterans Affairs (BX004249 and BX006040) , the American Heart Association Postdoctoral Fellowship (834962) , and the University of Iowa Fraternal Order of Eagles Diabetes Research Center.
- Language
- English
- Date published
- 06/2025
- Academic Unit
- Iowa Neuroscience Institute; Biostatistics; Fraternal Order of Eagles Diabetes Research Center; Neuroscience and Pharmacology; Internal Medicine
- Record Identifier
- 9984826346702771
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