scholarly journals Principal Components Analysis of Urine Caffeine and Caffeine Metabolites in the US Population

2015 ◽  
Vol 29 (S1) ◽  
Author(s):  
Ching‐I Pao ◽  
Michael Rybak ◽  
Maya Sternberg ◽  
Namanjeet Ahluwalia ◽  
Christine Pfeiffer
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
The Us ◽  
Components Analysis ◽  
Analysis Of Urine

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

A FAMILY OF MODELS EXPLAINING THE LEVEL-SLOPE-CURVATURE EFFECT

2003 ◽  
Vol 06 (03) ◽  
pp. 239-255 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Correlation Structure ◽  
Spectral Structure ◽  
Curvature Effect ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0 ≤ ρ ≤ 1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves and we extend the analysis to multiple curves.

ON THE SPECTRAL DECOMPOSITION OF EMPIRICAL CORRELATION MATRICES

2001 ◽  
Vol 10 (08) ◽  
pp. 1201-1213 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS F. TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Spectral Decomposition ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Empirical Correlation ◽  
Spectral Structure ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0≤ρ≤1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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