dbo:abstract
|
- The goal of S-estimators is to have a simple high-breakdown regression estimator, which share the flexibility and nice asymptotic properties of M-estimators. The name "S-estimators" was chosen as they are based on estimators of scale. We will consider estimators of scale defined by a function , which satisfy
* R1 – is symmetric, continuously differentiable and .
* R2 – there exists such that is strictly increasing on For any sample of real numbers, we define the scale estimate as the solution of , where is the expectation value of for a standard normal distribution. (If there are more solutions to the above equation, then we take the one with the smallest solution for s; if there is no solution, then we put .) Definition: Let be a sample of regression data with p-dimensional . For each vector , we obtain residuals by solving the equation of scale above, where satisfy R1 and R2. The S-estimator is defined by and the final scale estimator is then . (en)
|
dbo:wikiPageID
| |
dbo:wikiPageLength
|
- 2024 (xsd:nonNegativeInteger)
|
dbo:wikiPageRevisionID
| |
dbo:wikiPageWikiLink
| |
dcterms:subject
| |
rdfs:comment
|
- The goal of S-estimators is to have a simple high-breakdown regression estimator, which share the flexibility and nice asymptotic properties of M-estimators. The name "S-estimators" was chosen as they are based on estimators of scale. We will consider estimators of scale defined by a function , which satisfy
* R1 – is symmetric, continuously differentiable and .
* R2 – there exists such that is strictly increasing on For any sample of real numbers, we define the scale estimate as the solution of , Definition: and the final scale estimator is then . (en)
|
rdfs:label
| |
owl:sameAs
| |
prov:wasDerivedFrom
| |
foaf:isPrimaryTopicOf
| |
is dbo:wikiPageWikiLink
of | |
is foaf:primaryTopic
of | |