The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Bayesian-multiplicative treatment of count zeros in compositional data sets
Abstract
Compositional count data are discrete vectors representing the numbers of outcomes falling into any of several mutually exclusive categories. Compositional techniques based on the log-ratio methodology are appropriate in those cases where the total sum of the vector elements is not of interest. Such compositional count data sets can contain zero values which are often the result of insufficiently large samples. That is, they refer to unobserved positive values that may have been observed with a larger number of trials or with a different sampling design. Because the log-ratio transformations require data with positive values, any statistical analysis of count compositions must be preceded by a proper replacement of the zeros. A Bayesian-multiplicative treatment has been proposed for addressing this count zero problem in several case studies. This treatment involves the Dirichlet prior distribution as the conjugate distribution of the multinomial distribution and a multiplicative modification of the non-zero values. Different parameterizations of the prior distribution provide different zero replacement results, whose coherence with the vector space structure of the simplex is stated. Their performance is evaluated from both the theoretical and the computational point of view.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
The Statistical Analysis of Compositional Data
Aitchison, J. (1986) · doi:10.1007/978-94-009-4109-0 · Cited by 4,383
The Statistical Analysis of Compositional Data
Piepel, G. F. (1988) · Technometrics · doi:10.1080/00401706.1988.10488337 · Cited by 3,418
Inferring Correlation Networks from Genomic Survey Data
Friedman, J., & Alm, E. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002687 · Cited by 2,706
(2010) · Encyclopedia of Research Design · doi:10.4135/9781412961288.n40 · Cited by 1,995
Isometric Logratio Transformations for Compositional Data Analysis
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barceló-Vidal, C. (2003) · Mathematical Geology · doi:10.1023/a:1023818214614 · Cited by 1,935
Compositional Analysis of Habitat Use From Animal Radio‐Tracking Data
Aebischer, N. J., Robertson, P. A., & Kenward, R. E. (1993) · Ecology · doi:10.2307/1940062 · Cited by 1,909
Pearson, K. (1897) · Proceedings of the Royal Society of London · doi:10.1098/rspl.1896.0076 · Cited by 1,130
(2016) · Biostatistics in Public Health Using STATA · doi:10.1201/b19547-8 · Cited by 1,037
Compositional Data Analysis: Theory and Applications
(2011) · Wiley eBooks · doi:10.1002/9781119976462 · Cited by 704
Cited by
Normalization and microbial differential abundance strategies depend upon data characteristics
Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J. R., Vázquez-Baeza, Y., Birmingham, A., et al. (2017) · Microbiome · doi:10.1186/s40168-017-0237-y · Cited by 2,051
Palarea-Albaladejo, J., & Martín-Fernández, J. A. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.02.019 · Cited by 965
Self-citationMeta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet
Bisanz, J. E., Upadhyay, V., Turnbaugh, J. A., Ly, K., & Turnbaugh, P. J. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.06.013 · Cited by 333
A phylogenetic transform enhances analysis of compositional microbiota data
Silverman, J. D., Washburne, A. D., Mukherjee, S., & David, L. A. (2017) · eLife · doi:10.7554/elife.21887 · Cited by 319
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
Reichart, D., Lindberg, E. L., Maatz, H., Miranda, A. M. A., Viveiros, A., Shvetsov, N., Gärtner, A., Nadelmann, E. R., Lee, M., Kanemaru, K., et al. (2022) · Science · doi:10.1126/science.abo1984 · Cited by 240
Metadata
- Authors
- Martín-Fernández, J.-A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J.
- Venue
- Statistical Modelling
- Publisher
- SAGE Publications
- Published
- 2014-09-28
- Volume
- 15
- Issue
- 2
- Pages
- 134-158
- ISSN
- 1471-082X (ISSN-L)
- 1477-0342
- OpenAlex
- W2163169537
- Cited by
- 263
- References
- 31
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayesian-multiplicative treatment of count zeros in compositional data sets. https://app.cochat.ai/papers/10.1177/1471082x14535524
- The paper
- Martín-Fernández, J.-A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J. (2015). Bayesian-multiplicative treatment of count zeros in compositional data sets. Statistical Modelling. https://doi.org/10.1177/1471082x14535524
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
