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PARSIMONY JACKKNIFING OUTPERFORMS NEIGHBOR‐JOINING
Author(s) -
Farris James S.,
Albert Victor A.,
Källersjö Mari,
Lipscomb Diana,
Kluge Arnold G.
Publication year - 1996
Publication title -
cladistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.323
H-Index - 92
eISSN - 1096-0031
pISSN - 0748-3007
DOI - 10.1111/j.1096-0031.1996.tb00196.x
Subject(s) - bootstrapping (finance) , resampling , k nearest neighbors algorithm , mathematics , computer science , tree (set theory) , algorithm , jackknife resampling , statistics , combinatorics , artificial intelligence , econometrics , estimator
— Because they are designed to produced just one tree, neighbor‐joining programs can obscure ambiguities in data. Ambiguities can be uncovered by resampling, but existing neighbor‐joining programs may give misleading bootstrap frequencies because they do not suppress zero‐length branches and/or are sensitive to the order of terminals in the data. A new procedure, parsimony jackknifing, overcomes these problems while running hundreds of times faster than existing programs for neighbor‐joining bootstrapping. For analysis of large matrices, parsimony jackknifing is hundreds of thousands of times faster than extensive branch‐swapping, yet is better able to screen out poorly‐supported groups.