scholarly journals Use of principal component analysis for identification of temporal and spatial patterns in the dynamics of ionospheric equatorial anomaly

2015 ◽  
Vol 574 ◽  
pp. 012152
Author(s):  
Yu S Maslennikova ◽  
V V Bochkarev ◽  
D S Voloskov
Keyword(s):  
Principal Component Analysis ◽  
Spatial Patterns ◽  
Principal Component ◽  
Component Analysis ◽  
Equatorial Anomaly ◽  
Temporal And Spatial Patterns ◽  
Temporal And Spatial

scholarly journals An Eigenvalue Test for spatial Principal Component Analysis

2017 ◽  
Author(s):  
V. Montano ◽  
T. Jombart
Keyword(s):  
Principal Component Analysis ◽  
Spatial Patterns ◽  
Statistical Power ◽  
Principal Component ◽  
Component Analysis ◽  
Type I ◽  
Type I Errors ◽  
Spatial Principal Component Analysis ◽  
Local Test ◽  
Global And Local

AbstractBackgroundThe spatial Principal Component Analysis (sPCA, Jombart 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; Jombart et al. 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.ResultsWe compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components.ConclusionsAs such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.

scholarly journals Extremal dependence as given by the tail pairwise dependence matrix inprecipitation and temperature data

2021 ◽  
Author(s):  
Svenja Szemkus ◽  
Petra Friederichs
Keyword(s):  
Principal Component Analysis ◽  
Spatial Patterns ◽  
Heat Waves ◽  
Principal Component ◽  
Component Analysis ◽  
Extreme Weather ◽  
Temperature Data ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Pairwise Dependence

<p>A better understanding of the dynamics and impacts of extreme weather events and their changes due to climate change is the subject of the ClimXtreme project (climxtreme.net) funded by the German Federal Ministry of Education and Research. <br>The CoDEx project is investigating how data compression techniques can contribute to a better description and understanding of extremes. Various unsupervised learning approaches, such as clustering or principal component analysis, focusing on extremes have been developed recently and will be investigated and compared within the project. <br>We use principal component analysis to study the spatial (co-)occurrence during extreme weather events such as heavy precipitation, heat waves or droughts. The focus on extreme events is done by using the tail pairwise dependence matrix (TPDM), proposed by Cooley and Thibaud (2019) as an analogue to the covariance matrix for extremes. Since the simultaneous occurrence of precipitation deficits and high temperature played an important role, especially in heat waves, we explore how Cooley and Thibaud's concept can be used in this regard. We propose an estimation of the TPDM based on pairwise dependencies of two variables. A singular value decomposition gives us insight into the spatial co-occurrence of extreme spatial patterns, which contributes to the understanding of so-called compound events. <br>We use daily precipitation and temperature data, including observational stations and regional reanalyses in Germany and Europe. Using this method, we extract spatial patterns over Germany and Europe based on extreme dependencies. In addition, we identify historical events, and examine them in more detail in this context.</p>

scholarly journals Principal component analysis of rainwater composition at BAPMoN stations in India

MAUSAM ◽  
2022 ◽  
Vol 44 (2) ◽  
pp. 179-184
Author(s):  
B. MUKHOPADHYAY ◽  
S.S. SINGH ◽  
S. V. DATAR
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Distribution Characteristics ◽  
Temporal And Spatial Distribution ◽  
Rainwater Composition ◽  
Spatial Distribution Characteristics ◽  
Temporal And Spatial

Data from Indian BAPMoN stations were analyzed using the Principal Component Analysis (PCA) by examining broadly the temporal and spatial distribution characteristics of the ions, from mineral and gaseous sources, observed in rainwater samples collected over the Indian BAPMoN stations over along period (1976-87), The results show that the pH of rainwater can be generally explained In terms of the concentration of SO.-2 , NO3 -1, CI-l, Ca+2 and Na+1 ion~, However, other mechanisms could determine the overall nature of the Interactions, These mechanisms have become more clear by performing principal component analysis.

scholarly journals Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject

2021 ◽  
Author(s):  
Guanghui Zhang ◽  
Xueyan Li ◽  
Yingzhi Lu ◽  
Timo Tiihonen ◽  
Zheng Chang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Spatial Information ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Single Trial ◽  
Individual Subject ◽  
Spatial Pca ◽  
Related Potentials ◽  
Temporal And Spatial

AbstractTemporal principal component analysis (t-PCA) has been widely used to extract event-related potentials (ERPs) at the group level of multiple subjects’ ERP data. The key assumption of group t-PCA analysis is that desired ERPs of all subjects share the same waveforms (i.e., temporal components), whereas waveforms of different subjects’ ERPs can be variant in phases, peak latencies and so on, to some extent. Additionally, several PCA-extracted components coming from the same ERP dataset failed to be statistically analysed simultaneously because their polarities and amplitudes were indeterminate. To fill these gaps, a novel technique was proposed and employed to extract desired ERP from single-trial EEG dataset of an individual subject. Firstly, the dataset of all trials and all conditions of one subject were stacked across electrodes to form a matrix. Secondly, the temporal and spatial PCA-components were extracted from single-trial EEG dataset matrix for each subject by t-PCA and Promax rotation. Thirdly, the desired components were selected and projected to the electrode fields simultaneously to correct their variance and polarity indeterminacies. Next, single-trial EEG datasets of the back-projection were averaged to generate the waveforms of desired ERP for each subject and then amplitudes of the desired ERP were quantified. The yields indicated that the proposed approach can efficient exploit the temporal and spatial information of single-trial EEG data and can temporally filter the data to extract the desired ERPs for an individual subject.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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