Switch to
| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2
|
| pubmed:dateCreated |
1990-2-12
|
| pubmed:abstractText |
Change-point models, in which a linear or non-linear relation is generalized by allowing it to change at a point not fixed in advance, are of growing importance in allometric and other types of modeling. Frequently, the change-point is picked "by eye" and separate regressions are run for each resultant subdomain. This procedure is deficient, however, for the following reasons: first, a repeatable and objective procedure for estimating the change-point has not been used; second, the subsequent analysis usually does not take into account the fact that the change-point is estimated from the data; and last, the usually desirable requirement of continuity at the change-point is ignored. This paper describes various methods for jointly estimating linear relations and the intervening change-point from the data. In the simplest case, with normal errors and a linear relation of one variable upon another, this amounts to fitting a "bent line" via least squares techniques. In addition, tests and graphical diagnostics for the presence of change-points are presented. An example is given where a change-point and slopes are estimated for the relation of running speed with size among land mammals. In the past, these data have been fit with a straight line or a parabola. It is shown here that superior fit and interpretability are achieved using a change-point model.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
May
|
| pubmed:issn |
0022-5193
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:day |
22
|
| pubmed:volume |
138
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
235-56
|
| pubmed:dateRevised |
2006-11-15
|
| pubmed:meshHeading | |
| pubmed:year |
1989
|
| pubmed:articleTitle |
Fitting bent lines to data, with applications to allometry.
|
| pubmed:affiliation |
Department of Statistics, University of Chicago, Illinois 60637.
|
| pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.
|