Demo Data from EDBT 2026 / reviewers in the wild / expert
M. Equihua
dblp:55/6505
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1988
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
biological classification |
0.0 | 1 | 1988 | Analysis of finite mixture of distributions: a statistical tool for biological classification problems · Comput. Appl. Biosci. 1988 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood estimation · 0.0discriminant analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | Analysis of finite mixture of distributions: a statistical tool for biological classification problemsabstractA microcomputer program to analyze finite mixtures of normal, binomial, Poisson or exponential distributions by a maximum likelihood estimation procedure is described. The program is coded in Turbo Pascal. Some theoretical and practical aspects of the compound distributions are discussed, mainly mathematical characteristics, fitting procedures and tests of hypotheses. The statistical tool, which is a cluster analysis technique, is presented in a general context for applications in biology. In particular an ecological example is briefly described: the age class structure resolution of a white-tailed deer population. To improve the usefulness for classification purposes, the link with discriminant analysis is examined. For a successful analysis of finite mixture of distributions the need for a large sample size is emphasized. M. Equihua |
Comput. Appl. Biosci. | 1 |