Linked Life Data

18481513

Source:http://linkedlifedata.com/resource/pubmed/id/18481513

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2008-5-16
pubmed:abstractText
Power-law frequency distributions characterize a wide array of natural phenomena. In ecology, biology, and many physical and social sciences, the exponents of these power laws are estimated to draw inference about the processes underlying the phenomenon, to test theoretical models, and to scale up from local observations to global patterns. Therefore, it is essential that these exponents be estimated accurately. Unfortunately, the binning-based methods traditionally used in ecology and other disciplines perform quite poorly. Here we discuss more sophisticated methods for fitting these exponents based on cumulative distribution functions and maximum likelihood estimation. We illustrate their superior performance at estimating known exponents and provide details on how and when ecologists should use them. Our results confirm that maximum likelihood estimation outperforms other methods in both accuracy and precision. Because of the use of biased statistical methods for estimating the exponent, the conclusions of several recently published papers should be revisited.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0012-9658
pubmed:author
pubmed:issnType
Print
pubmed:volume
89
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
905-12
pubmed:dateRevised
2008-10-30
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
On estimating the exponent of power-law frequency distributions.
pubmed:affiliation
Department of Biology and the Ecology Center, Utah State University, Logan, Utah 84322, USA. epwhite@biology.usu.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.