Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.

Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measure...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ana Severiano, João A Carriço, D Ashley Robinson, Mário Ramirez, Francisco R Pinto
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5c93b05bf72d47a583b594db0b626a43
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5c93b05bf72d47a583b594db0b626a43
record_format	dspace
spelling	oai:doaj.org-article:5c93b05bf72d47a583b594db0b626a432021-11-18T06:53:47ZEvaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.1932-620310.1371/journal.pone.0019539https://doaj.org/article/5c93b05bf72d47a583b594db0b626a432011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21611165/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measures, including pairwise agreement measures. Several measures have been proposed and the importance of obtaining confidence intervals for the point estimate in the comparison of these measures has been highlighted. A broad range of methods can be used for the estimation of confidence intervals. However, evidence is lacking about what are the appropriate methods for the calculation of confidence intervals for most clustering agreement measures. Here we evaluate the resampling techniques of bootstrap and jackknife for the calculation of the confidence intervals for clustering agreement measures. Contrary to what has been shown for some statistics, simulations showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low. The coverage of the jackknife confidence interval is robust to changes in cluster number and cluster size distribution.Ana SeverianoJoão A CarriçoD Ashley RobinsonMário RamirezFrancisco R PintoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 5, p e19539 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ana Severiano João A Carriço D Ashley Robinson Mário Ramirez Francisco R Pinto Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
description	Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measures, including pairwise agreement measures. Several measures have been proposed and the importance of obtaining confidence intervals for the point estimate in the comparison of these measures has been highlighted. A broad range of methods can be used for the estimation of confidence intervals. However, evidence is lacking about what are the appropriate methods for the calculation of confidence intervals for most clustering agreement measures. Here we evaluate the resampling techniques of bootstrap and jackknife for the calculation of the confidence intervals for clustering agreement measures. Contrary to what has been shown for some statistics, simulations showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low. The coverage of the jackknife confidence interval is robust to changes in cluster number and cluster size distribution.
format	article
author	Ana Severiano João A Carriço D Ashley Robinson Mário Ramirez Francisco R Pinto
author_facet	Ana Severiano João A Carriço D Ashley Robinson Mário Ramirez Francisco R Pinto
author_sort	Ana Severiano
title	Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
title_short	Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
title_full	Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
title_fullStr	Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
title_full_unstemmed	Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
title_sort	evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/5c93b05bf72d47a583b594db0b626a43
work_keys_str_mv	AT anaseveriano evaluationofjackknifeandbootstrapfordefiningconfidenceintervalsforpairwiseagreementmeasures AT joaoacarrico evaluationofjackknifeandbootstrapfordefiningconfidenceintervalsforpairwiseagreementmeasures AT dashleyrobinson evaluationofjackknifeandbootstrapfordefiningconfidenceintervalsforpairwiseagreementmeasures AT marioramirez evaluationofjackknifeandbootstrapfordefiningconfidenceintervalsforpairwiseagreementmeasures AT franciscorpinto evaluationofjackknifeandbootstrapfordefiningconfidenceintervalsforpairwiseagreementmeasures
_version_	1718424231329398784

Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures.

Ejemplares similares