1.
Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies
by Aschard, Hugues
American journal of human genetics, 2014, Vol.94 (5), p.662-676
2.
Use of Raman spectroscopy and chemometrics to distinguish blue ballpoint pen inks
by de Souza Lins Borba, Flávia
Forensic science international, 2015, Vol.249, p.73-82
3.
Selected statistical methods of data analysis for multivariate functional data
by Gorecki, Tomasz
Statistical papers (Berlin, Germany), 2016, Vol.59 (1), p.153-182
4.
Comparison of discrete-point vs. dimensionality-reduction techniques for describing performance-related aspects of maximal vertical jumping
by Richter, Chris
Journal of biomechanics, 2014, Vol.47 (12), p.3012-3017
5.
Correlation structure and principal components in the global crude oil market
by Dai, Yue-Hua
Empirical economics, 2016, Vol.51 (4), p.1501-1519
6.
Sparse common component analysis for multiple high-dimensional datasets via noncentered principal component analysis
by Park, Heewon
Statistical papers (Berlin, Germany), 2018, Vol.61 (6), p.2283-2311
7.
Neonates with reduced neonatal lung function have systemic low-grade inflammation
by Chawes, Bo L.K., MD, PhD
Journal of allergy and clinical immunology, 2014, Vol.135 (6), p.1450-1456.e1
8.
On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use
by Gaskin, Cadeyrn J
International journal of nursing studies, 2014, Vol.51 (3), p.511-521
9.
Comparative Analysis of Principal Components Can be Misleading
by Uyeda, Josef C
Systematic biology, 2015, Vol.64 (4), p.677-689
10.
A plug-in approach to sparse and robust principal component analysis
by Greco, Luca
Test (Madrid, Spain), 2015, Vol.25 (3), p.449-481
11.
Inflammatory endotypes of chronic rhinosinusitis based on cluster analysis of biomarkers
by Tomassen, Peter, MD
Journal of allergy and clinical immunology, 2016, Vol.137 (5), p.1449-1456.e4
12.
Automatic drought stress detection in grapevines without using conventional threshold values
by Baert, Annelies
Plant and soil, 2013, Vol.369 (1/2), p.439-452
13.
What is principal component analysis?
by RINGNER, Markus
Nature biotechnology, 2008, Vol.26 (3), p.303-304
14.
Principal component selection via adaptive regularization method and generalized information criterion
by Park, Heewon
Statistical papers (Berlin, Germany), 2015, Vol.58 (1), p.147-160
15.
Moving toward endotypes in atopic dermatitis: Identification of patient clusters based on serum biomarker analysis
by Thijs, Judith L., MD
Journal of allergy and clinical immunology, 2017, Vol.140 (3), p.730-737
16.
Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification
by Tabassian, Mahdi
The International Journal of Cardiovascular Imaging, 2017, Vol.33 (8), p.1159-1167
17.
Principal points for an allometric extension model
by Matsuura, Shun
Statistical papers (Berlin, Germany), 2013, Vol.55 (3), p.853-870
18.
Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
by Galinsky, Kevin J
American journal of human genetics, 2016, Vol.98 (3), p.456-472
19.
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis
by Williams, Alex H
Neuron (Cambridge, Mass.), 2018, Vol.98 (6), p.1099-1115.e8
20.
A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index
by Shi, Xiangnan
Journal of biomechanics, 2014, Vol.47 (10), p.2277-2285
