scholarly journals COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1237
Author(s):  
Christian Acal ◽  
Manuel Escabias ◽  
Ana M. Aguilera ◽  
Mariano J. Valderrama
Keyword(s):  
Intensive Care ◽  
Principal Components ◽  
Missing Values ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Equation ◽  
Functional Responses ◽  
Principal Components Regression ◽  
Functional Principal Components ◽  
The Relationship

The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.

Modeling relevance of mobile communication services by social setting dimensions

2016 ◽  
Vol 20 (1) ◽  
pp. 311-331
Author(s):  
Elena Menichelli ◽  
Richard Ling
Keyword(s):  
Mobile Communication ◽  
Communication Channel ◽  
Mixed Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Communication Services ◽  
Channel Choice ◽  
Social Settings ◽  
Negative Messages ◽  
The Relationship

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.

scholarly journals Age-seroprevalence curves for the multi-strain structure of influenza A virus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dao Nguyen Vinh ◽  
Nguyen Thi Duy Nhat ◽  
Erwin de Bruin ◽  
Nguyen Ha Thao Vy ◽  
Tran Thi Nhu Thao ◽  
...  
Keyword(s):  
Principal Components ◽  
Attack Rate ◽  
Influenza A ◽  
Principal Component ◽  
Human Antibody ◽  
Birth Cohorts ◽  
Serum Samples ◽  
The Relationship ◽  
Subtype H1 ◽  
Variable Group

AbstractThe relationship between age and seroprevalence can be used to estimate the annual attack rate of an infectious disease. For pathogens with multiple serologically distinct strains, there is a need to describe composite exposure to an antigenically variable group of pathogens. In this study, we assay 24,402 general-population serum samples, collected in Vietnam between 2009 to 2015, for antibodies to eleven human influenza A strains. We report that a principal components decomposition of antibody titer data gives the first principal component as an appropriate surrogate for seroprevalence; this results in annual attack rate estimates of 25.6% (95% CI: 24.1% – 27.1%) for subtype H3 and 16.0% (95% CI: 14.7% – 17.3%) for subtype H1. The remaining principal components separate the strains by serological similarity and associate birth cohorts with their particular influenza histories. Our work shows that dimensionality reduction can be used on human antibody profiles to construct an age-seroprevalence relationship for antigenically variable pathogens.

scholarly journals Principal component regression of carcass traits in meat line funaab alpha chicken genotype

2021 ◽  
Author(s):  
Mathew Wheto ◽  
Nkiruka Goodness Chima ◽  
Henry T Ojoawo ◽  
Matthew A Adeleke ◽  
Sunday O Peters ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Principal Component Regression ◽  
Principal Component ◽  
Liver Weight ◽  
Morphometric Traits ◽  
Mean Values ◽  
Weight Prediction ◽  
The Relationship ◽  
Single Predictor ◽  
Variance Explained

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.

Effects of display curvature and hand length on smartphone usability

2017 ◽  
Vol 61 (1) ◽  
pp. 1054-1057 ◽  
Author(s):  
Jihhyeon Yi ◽  
Sungryul Park ◽  
Juah Im ◽  
Seonyeong Jeon ◽  
Gyouhyung Kyung
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
The Other ◽  
Side Edge ◽  
Hand Size ◽  
Size Groups ◽  
The Mean ◽  
Hand Length ◽  
Video Calling

The purpose of this study was to examine the effects of display curvature and hand length on smartphone usability, which was assessed in terms of grip comfort, immersive feeling, typing performance, and overall satisfaction. A total of 20 younger individuals with the mean (SD) age of 20.8 (2.4) yrs were divided into three hand-size groups (small: 8, medium: 6, large: 6). Two smartphones of the same size were used – one with a flat display and the other with a side-edge curved display. Three tasks (watching video, calling, and texting) were used to evaluate smartphone usability. The smartphones were used in a landscape mode for the first task, and in a portrait mode for the other two. The flat display smartphone provided higher grip comfort during calling (p = 0.008) and texting (p = 0.006) and higher overall satisfaction (p = 0.0002) than the curved display smartphone. The principal component regression (adjusted R2 = 0.49) of overall satisfaction on three principal components comprised of the remaining measures showed that the first principal component on grip comfort was more important than the other two on watching experience and texting performance. It is thus necessary to carefully consider the effect of display curvature on grip comfort when applying curved displays to hand-held devices such as smartphones.

scholarly journals Analysis of the delivery plane in the golf swing using principal components

2018 ◽  
Vol 232 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Andrew Morrison ◽  
Denise McGrath ◽  
Eric S Wallace
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Principal Components ◽  
Principal Component ◽  
Golf Swing ◽  
Ball Impact ◽  
Impact Location ◽  
The Relationship

Although the swing plane has been a popular area of golf biomechanics research, the movement of the club relative to the swing plane has yet to be shown experimentally to have a relationship with performance. This study used principal component and subsequent multiple regression analysis to investigate the relationship between the movement of the club relative to the delivery plane and clubhead characteristics at ball impact. The principal components reflected deviations from an individual swing plane, and lower values of these components were associated with less variability in the clubface impact location. In the event that a golf coach wants to improve the precision of ball striking, the results from this study suggest that both simplicity of the route and alignment of the club to the final trajectory before impact could be advantageous. However, this does not suggest that the technique should be based on a ‘model’ swing plane.

Principal Component Regression for Mixture Resolution in Control Analysis by UV-Visible Spectrophotometry

1994 ◽  
Vol 48 (1) ◽  
pp. 37-43 ◽  
Author(s):  
M. Blanco ◽  
J. Coello ◽  
H. Iturriaga ◽  
S. Maspoch ◽  
M. Redon
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optimal Number ◽  
Control Analysis ◽  
Baseline Drift ◽  
Visible Spectrophotometry ◽  
Calibration Matrix ◽  
Uv Visible ◽  
Diode Array Spectrophotometer

The potential of principal component regression (PCR) for mixture resolution by UV-visible spectrophotometry was assessed. For this purpose, a set of binary mixtures with Gaussian bands was simulated, and the influence of spectral overlap on the precision of quantification was studied. Likewise, the results obtained in the resolution of a mixture of components with extensively overlapped spectra were investigated in terms of spectral noise and the criterion used to select the optimal number of principal components. The model was validated by cross-validation, and the number of significant principal components was determined on the basis of four different criteria. Three types of noise were considered: intrinsic instrumental noise, which was modeled from experimental data provided by an HP 8452A diode array spectrophotometer; constant baseline shifts; and baseline drift. Introducing artificial baseline alterations in some samples of the calibration matrix was found to increase the reliability of the proposed method in routine analysis. The method was applied to the analysis of mixtures of Ti, AI, and Fe by resolving the spectra of their 8-hydroxyquinoline complexes previously extracted into chloroform.

scholarly journals Clustering of ingredients with amino acid composition similar to the nutritional requirement of Nile tilapia

2010 ◽  
Vol 67 (5) ◽  
pp. 517-523 ◽  
Author(s):  
Álvaro José de Almeida Bicudo ◽  
Luis Fernando Batista Pinto ◽  
José Eurico Possebon Cyrino
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Principal Components ◽  
Nile Tilapia ◽  
Principal Component ◽  
Essential Amino Acids ◽  
Amino Acid Profile ◽  
Nutritional Requirement ◽  
Dietary Amino Acids ◽  
The Relationship

The search for balanced diets, which may elicit improved growth of fish, requires appropriate selection of available protein sources. This study aims at clustering feedstuffs according to amino acid profile, determining which ones show essential amino acids (EAA) profiles closer to the ideal dietary amino acids requirements of Nile tilapia (Oreochromis niloticus), and studying the relationship among amino acids feedstuffs groups. Tabled data on EAA more cystine and tyrosine, in relation to lysine contents, of 40 feedstuffs ordinarily used to formulate fish diets were studied. Feedstuffs were grouped according to amino acids profile by cluster analysis of Euclidean distances. The principal components analysis was used to determine the relationship among amino acids in each feedstuff group. Three groups of ingredients were parted and two ingredients, low tannin sorghum and corn gluten meal 60%, did not go with any group. Dietary amino acids requirements of Nile tilapia were similar to the amino acid profile of 22 feedstuffs. The principal component analysis explained with three principal components more than 75% of total variance of amino acids in three feedstuff groups. Therefore, until additional, detailed information on amino acids availability of different ingredients is consolidated, total amino acids profiles will continue to be important information to select and use conventional or surrogate ingredients for formulating and processing feeds for tilapia.

Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion

2013 ◽  
Vol 38 (1) ◽  
pp. 39-45
Author(s):  
Peng Song ◽  
Li Zhao ◽  
Yongqiang Bao
Keyword(s):  
Principal Components ◽  
Median Filter ◽  
Principal Component Regression ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Voice Conversion ◽  
Perceptual Quality ◽  
Posterior Probabilities ◽  
Spectral Mapping ◽  
The One

Abstract The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.

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