scholarly journals Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis

2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Hiroyuki Yamamoto ◽  
Tamaki Fujimori ◽  
Hajime Sato ◽  
Gen Ishikawa ◽  
Kenjiro Kami ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Hypothesis Testing ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Factor Loading ◽  
Component Analysis ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Metabolite Set Enrichment Analysis

scholarly journals Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

2016 ◽  
Vol 101 ◽  
pp. 45-54 ◽  
Author(s):  
Francesc Pozo ◽  
Yolanda Vidal
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Hypothesis Testing ◽  
Wind Turbine ◽  
Principal Component ◽  
Component Analysis ◽  
Angle Measurement ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Detection Scheme

This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.

scholarly journals OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 149
Author(s):  
Hiroyuki Yamamoto ◽  
Yasumune Nakayama ◽  
Hiroshi Tsugawa
Keyword(s):  
Principal Component Analysis ◽  
Hypothesis Testing ◽  
Time Course ◽  
Rank Order ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Pca Method ◽  
Metabolome Data

Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data.

IDENTIFICATION OF KNEE FRONTAL PLANE KINEMATIC PATTERNS IN NORMAL GAIT BY PRINCIPAL COMPONENT ANALYSIS

2013 ◽  
Vol 13 (03) ◽  
pp. 1350026 ◽  
Author(s):  
NEILA MEZGHANI ◽  
ALEXANDRE FUENTES ◽  
NATHALY GAUDREAULT ◽  
AMAR MITICHE ◽  
RACHID AISSAOUI ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Stance Phase ◽  
Large Population ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Swing Phase ◽  
Statistical Hypothesis ◽  
Gait Patterns ◽  
Phase Cluster

The purpose of this study was to identify meaningful gait patterns in knee frontal plane kinematics from a large population of asymptomatic individuals. The proposed method used principal component analysis (PCA). It first reduced the data dimensionality, without loss of relevant information, by projecting the original kinematic data onto a subspace of significant principal components (PCs). This was followed by a discriminant model to separate the individuals' gait into homogeneous groups. Four descriptive gait patterns were identified and validated by clustering silhouette width and statistical hypothesis testing. The first pattern was close to neutral during the stance phase and in adduction during the swing phase (Cluster 1). The second pattern was in abduction during the stance phase and tends into adduction during the swing phase (Cluster 2). The third pattern was close to neutral during the stance phase and in abduction during the swing phase (Cluster 3) and the fourth was in abduction during both the stance and the swing phase (Cluster 4).

ОЦЕНКА ПРИГОДНОСТИ СРЕДЫ, СОЗДАННОЙ НА УРОВНЕ СООБЩЕСТВА, ДЛЯ ЛЮДЕЙ ПОЖИЛОГО ВОЗРАСТА, ВЕДУЩИХ АКТИВНЫЙ ОБРАЗ ЖИЗНИ: ОСНОВНЫЕ РЕЗУЛЬТАТЫ ПСИХОМЕТРИЧЕСКОГО ТЕСТА

2020 ◽  
pp. 153-158
Author(s):  
N. Asiamah ◽  
K. Kouveliotis ◽  
R. Eduafo ◽  
R. Borkey
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Factor Loading ◽  
Component Analysis ◽  
Built Environments ◽  
Future Assessment ◽  
Confirmatory Factor ◽  
High Maturity ◽  
Good Internal Consistency ◽  
Development Approach

The community development approach to healthy active aging advocates constant availability of relevant built environment factors in the community as a requirement for active behaviors. In this paper, scores from a new scale measuring these factors are interpreted to guide future assessment of active built environments in the community. Participants were 515 older adults in Accra aged 60 years or more who met some inclusion criteria. Principal component analysis (with varimax rotation) and confirmatory factor analysis were used to select relevant items and assess the psychometric properties of the final scale. Principal component analysis produced a six-factor solution with 33 items, and the final scale had a good internal consistency (Cronbach’s α=0,96; factor loading ≥0,50). The minimum and maximum scores of the scale are 33 and 99 respectively, and the scale indicates low maturity, moderate maturity and high maturity of walkable built environments with scores of 1-30, 34-66 and 67- 99 respectively. The final scale, hereby called ACTIVE COMMUNITY, can be used to assess the maturity of active built environments to understand how community design projects are impacting active behaviors over time. Для здорового активного старения необходимы соответствующие условия среды обитания, которые способствуют активному поведению. Новая шкала, характеризующая данные факторы, позволяет оценивать активность созданной человеком среды в обществе. Участниками исследования были 515 человек пожилого возраста (60 лет и старше) из Аккры. Для выбора соответствующих элементов и оценки психометрических свойств окончательной шкалы был использован анализ основных компонентов (с вращением факторов методом Varimax) и подтверждающий факторный анализ. Анализ основных компонентов позволил получить шестифакторное решение с 33 пунктами, а итоговая шкала имела хорошую внутреннюю согласованность (α-коэффициент Кронбаха 0,96; коэффициент нагрузки ≥0,50). Минимальные и максимальные оценки по шкале составили 33 и 99 соответственно; шкала показывает низкую, умеренную и высокую пригодность для прогулок в урбанизированной среде с оценками 1-30, 34-66 и 67-99 соответственно. Шкалу, характеризующую АКТИВНОЕ ОБЩЕСТВО, можно использовать для оценки пригодности активной созданной человеком среды, чтобы понять, как разработки общества влияют на активное поведение с течением времени.

scholarly journals Indonesian Provinces SDGs Composite Index: Lampung Province Analysis

2021 ◽  
Vol 67 (1) ◽  
pp. 1
Author(s):  
Reny Andriati ◽  
Arief Anshory Yusuf
Keyword(s):  
Principal Component Analysis ◽  
Sustainable Development Goals ◽  
National Level ◽  
Composite Index ◽  
Principal Component ◽  
Factor Loading ◽  
Component Analysis ◽  
Provincial Level ◽  
Development Goals ◽  
Economic Survey

Publications of Sustainable Development Goals (SDGs) have mainly been conducted at a national level and  separately for each goal. No prior research has been done on SDGs composite index at a provincial level in Indonesia. It is necessary to create a composite index that presents a single value at the provincial level to enable regional evaluation. The Indonesia Province SDGs composite index is developed from indicators based on Statistics Indonesia gathered from several publications. The data sources are the National Socio-Economic Survey (Susenas) and the Basic Health Research (Riskesdas) which were linked surveys held in 2018. Principal Component Analysis and Factor Analysis are used as the methods to select the indicators of the SDGs. Those selected indicators are then normalized using the min-max method and subsequently weighted using factor loading derived from the principal component analysis. Finally, the indicators are aggregated using an arithmetic mean to determine the composite index. The Indonesia Province SDGs composite index is an approach to measure achievement of SDGs agenda. In addition, each goal achievement is summarized as a goal index. The SDGs composite index for Lampung Province is 52.2%, meaning that Lampung Province is 52.2% of the way to fully achieving the SDGs, according to the measures used to calculate this index. The findings on goal index suggest that development is highly requested on public services such as housing and water supply. 

scholarly journals RESEARCH AND MODELING OF THE UNEMPLOYMENT IN UKRAINE: CORRELATION-REGRESSION ANALYSIS

2021 ◽  
Vol 26 (3(88)) ◽  
Author(s):  
Yana Dovhenko ◽  
Zoia Khaletska ◽  
Lyudmila Yaremenko
Keyword(s):  
Principal Component Analysis ◽  
Unemployment Rate ◽  
Dynamic Models ◽  
Desirability Function ◽  
Principal Component ◽  
Factor Loading ◽  
Component Analysis ◽  
Macroeconomic Factors ◽  
Active Population ◽  
The Unemployed

The aim of the study is to conduct a statistical analysis of the modern labor market and adapt a multivariate econometric model of unemployment in Ukraine using the principal component analysis. The paper investigated the current state of unemployment in Ukraine for the last two decades. The dynamics of the unemployment rate and employment of the economically active population in Ukraine is analyzed. The analysis of the structure of the unemployed for reasons of dismissal and the tendency of changes in the size of the working age population is carried out. The gender aspect of the number of the unemployed population is investigated. Comprehensive assessments of the resources of labor potential by regions have been calculated and a rating of regions has been built. The disproportionality behind the Harrington`s desirability function was analyzed taking into account the factors of stimulants and de-stimulants. The rating assessment of unemployment for regional labor markets of Ukraine is given for the gradation of values of the desirability function. The main macroeconomic factors of influence on the level of unemployment in Ukraine have been determined. Structural and correlation-regression relationships have been analyzed. The identification of the model has been carried out. The multivariate unemployment model was adapted. The factorial database was checked for the presence of multicollinearity behind the Ferrer - Glober algorithm based on the criteria: Fisher, Spearman and Student. With the help of component analysis, the study of the relationship between factor variables was carried out. The factor loading matrix was constructed and analyzed. The matrix of the values of the principal components was calculated. The model of unemployment is constructed by the principal component analysis. The model was tested for adequacy, its economic content was analyzed. The residuals (random variables) are estimated to establish the quality of the constructed multivariate model. Dynamic models of factorial variables were built and their values for the next year were estimated. Through the normalized values of estimates of factor variables for dynamic models, the unemployment rate in Ukraine for the future (2021) was calculated.

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