Linked Life Data

7981399

Source:http://linkedlifedata.com/resource/pubmed/id/7981399

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1995-1-3
pubmed:abstractText
Time series that arise from biological experimentation can exhibit seasonality where the lengths of the seasons may vary. In addition, such time series may not be stationary with respect to either mean, variance, or autocorrelation, thus making the usual waveform-fitting techniques inappropriate. An agglomerative clustering algorithm for identifying seasons in such series is proposed, consisting of an initialization step, iterative steps where clusters are combined into larger clusters, and a stopping rule for the iteration. The clusters can be associated with seasons or phases, and biological cycles can be identified from the phases. Results of a simulation and an analysis of luteinizing hormone concentrations are presented.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
798-812
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
Identification of aperiodic seasonality in non-Gaussian time series.
pubmed:affiliation
Department of Biostatistics, University of Michigan, Ann Arbor 48109-2029.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.