| Title |
Automated discovery of test statistics using genetic programming
|
|---|---|
| Published in |
Genetic Programming and Evolvable Machines, October 2018
|
| DOI | 10.1007/s10710-018-9338-z |
| Pubmed ID | |
| Authors |
Jason H. Moore, Randal S. Olson, Yong Chen, Moshe Sipper |
| Abstract |
The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about the desirable properties of a good test statistic in addition to having an engine that can develop and explore candidate mathematical solutions with an intuitive representation. In this paper we describe a genetic programming-based system for the automated discovery of new test statistics. Specifically, our system was able to discover test statistics as powerful as the t-test for comparing sample means from two distributions with equal variances. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 29% |
| Switzerland | 1 | 6% |
| Czechia | 1 | 6% |
| Italy | 1 | 6% |
| Nepal | 1 | 6% |
| Philippines | 1 | 6% |
| India | 1 | 6% |
| Sweden | 1 | 6% |
| Unknown | 5 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 71% |
| Scientists | 5 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 18% |
| Researcher | 2 | 18% |
| Student > Postgraduate | 1 | 9% |
| Unknown | 6 | 55% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Mathematics | 1 | 9% |
| Computer Science | 1 | 9% |
| Medicine and Dentistry | 1 | 9% |
| Engineering | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 6 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 October 2019.
All research outputs
#4,205,368
of 25,364,603 outputs
Outputs from Genetic Programming and Evolvable Machines
#9
of 129 outputs
Outputs of similar age
#77,923
of 356,920 outputs
Outputs of similar age from Genetic Programming and Evolvable Machines
#3
of 5 outputs
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