Literature DB >> 7027855

Numerical evaluation of cytologic data. IX. Search for data structure by principal components transformation.

P H Bartels.   

Abstract

Principal components transformation may be used to explore the structure of a p-dimensional data set. It is difficult to detect inhomogeneities in a data set of multivariate variables by mere visual inspection of the numerical data. Plotting each variable's distribution is often either impractical, due to the number of variables involved, or might fail to reveal the presence of subpopulations due to high correlations. A practical example is given in which principal components transformation revealed the presence of subpopulations in a four-dimensional data set.

Mesh：

Year:  1981        PMID： 7027855

Source DB:  PubMed          Journal:  Anal Quant Cytol        ISSN： 0190-0471


  8 in total

1.  Unrestricted principal components analysis of brain electrical activity: issues of data dimensionality, artifact, and utility.

Authors:  F H Duffy; K Jones; P Bartels; G McAnulty; M Albert
Journal:  Brain Topogr       Date:  1992       Impact factor: 3.020

Review 2.  Quantified neurophysiology with mapping: statistical inference, exploratory and confirmatory data analysis.

Authors:  F H Duffy; K Jones; P Bartels; M Albert; G B McAnulty; H Als
Journal:  Brain Topogr       Date:  1990       Impact factor: 3.020

3.  Brain electrical correlates of psychological measures: strategies and problems.

Authors:  F H Duffy; G B McAnulty; K Jones; H Als; M Albert
Journal:  Brain Topogr       Date:  1993       Impact factor: 3.020

4.  A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study.

Authors:  Frank H Duffy; Heidelise Als
Journal:  BMC Med       Date:  2012-06-26       Impact factor: 8.775

5.  The relationship of Asperger's syndrome to autism: a preliminary EEG coherence study.

Authors:  Frank H Duffy; Aditi Shankardass; Gloria B McAnulty; Heidelise Als
Journal:  BMC Med       Date:  2013-07-31       Impact factor: 8.775

6.  EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients--a case control study.

Authors:  Frank H Duffy; Gloria B McAnulty; Michelle C McCreary; George J Cuchural; Anthony L Komaroff
Journal:  BMC Neurol       Date:  2011-07-01       Impact factor: 2.474

7.  A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study.

Authors:  Frank H Duffy; Aditi Shankardass; Gloria B McAnulty; Heidelise Als
Journal:  BMC Med       Date:  2017-03-09       Impact factor: 8.775

8.  Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls--first steps in development of a biomarker.

Authors:  Frank H Duffy; Eugene D'Angelo; Alexander Rotenberg; Joseph Gonzalez-Heydrich
Journal:  BMC Med       Date:  2015-11-02       Impact factor: 8.775
  8 in total

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