Demo Data from EDBT 2026 / reviewers in the wild / expert
J. O. Coleman
dblp:120/5840
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1982
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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.
| Theoretical computer science
1 paper |
Information theory · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › hypothesis testing
sequential hypothesis testing |
0.0 | 1 | 1982 | Repeated Hypothesis Testing on a Growing Data Set · IEEE Trans. Pattern Anal. Mach. Intell. 1982 |
Information theory
statistical inference |
0.0 | 1 | 1982 | Repeated Hypothesis Testing on a Growing Data Set · IEEE Trans. Pattern Anal. Mach. Intell. 1982 |
Methods — techniques the papers use, named apart from their topics
hypothesis testing · 0.0asymptotic analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1982 | Repeated Hypothesis Testing on a Growing Data SetabstractIn many problems, especially when data are collected over a long period of time, hypothesis testing is done repeatedly as new data arrive. It is shown both for one particular problem and for a more general class of problems that if testing is performed each time on the total amount of data accumulated, the true simple null hypothesis will be rejected at least once as the number of tests approaches infinity. Furthermore, it is conjectured that the conclusion holds for most problems of interest in which the null hypothesis is simple. Gerard V. Trunk, J. O. Coleman |
IEEE Trans. Pattern Anal. Mach. Intell. | 2 |