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J. O. Coleman

dblp:120/5840 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Information theory › hypothesis testing
sequential hypothesis testing
0.011982
Repeated Hypothesis Testing on a Growing Data Set · IEEE Trans. Pattern Anal. Mach. Intell. 1982
Information theory
statistical inference
0.011982
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
YearPublicationVenuePosition
1982 Repeated Hypothesis Testing on a Growing Data Set
abstract
In 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