scholarly journals Operational Risk Aggregation Based on Business Line Dependence: A Mutual Information Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wenzhou Wang ◽  
Limeng Shi ◽  
Xiaoqian Zhu
Keyword(s):  
Mutual Information ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Risk Estimation ◽  
Pearson Correlation ◽  
Operational Risk ◽  
Random Variable ◽  
Data Set ◽  
Risk Aggregation ◽  
Business Line

The dependencies between different business lines of banks have serious effects on the accuracy of operational risk estimation. Furthermore, the dependencies are far more complicated than simple linear correlation. While Pearson correlation coefficient is constructed based on the hypothesis of a linear association, the mutual information that measures all the information of a random variable contained in another random variable is a powerful alternative. Based on mutual information, the generalized correlation coefficient which can capture both linear and nonlinear correlation can be derived. This paper models the correlation between business lines by mutual information and normal copula. The experiment on a real-world Chinese bank operational risk data set shows that using mutual information to model the dependencies between business lines is more reasonable than linear correlation.

scholarly journals Measuring Independence between Statistical Randomness Tests by Mutual Information

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 741
Author(s):  
Jorge Augusto Karell-Albo ◽  
Carlos Miguel Legón-Pérez  ◽  
Evaristo José Madarro-Capó  ◽  
Omar Rojas ◽  
Guillermo Sosa-Gómez
Keyword(s):  
Mutual Information ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Statistical Dependency ◽  
Nonlinear Correlations ◽  
Randomness Tests ◽  
Dependency Detection

The analysis of independence between statistical randomness tests has had great attention in the literature recently. Dependency detection between statistical randomness tests allows one to discriminate statistical randomness tests that measure similar characteristics, and thus minimize the amount of statistical randomness tests that need to be used. In this work, a method for detecting statistical dependency by using mutual information is proposed. The main advantage of using mutual information is its ability to detect nonlinear correlations, which cannot be detected by the linear correlation coefficient used in previous work. This method analyzes the correlation between the battery tests of the National Institute of Standards and Technology, used as a standard in the evaluation of randomness. The results of the experiments show the existence of statistical dependencies between the tests that have not been previously detected.

scholarly journals Modified Pearson Correlation Coefficient for Two-color Imaging in Spherocylindrical Cells

2018 ◽  
Author(s):  
Sonisilpa Mohapatra ◽  
James C. Weisshaar
Keyword(s):  
Correlation Coefficient ◽  
Linear Correlation ◽  
Pearson Correlation ◽  
Three Dimensional ◽  
Mean Value ◽  
Pearson Correlation Coefficient ◽  
Bacterial Cells ◽  
Cell Geometry ◽  
Cell Shapes ◽  
Special Case

AbstractThe revolution in fluorescence microscopy enables sub-diffraction-limit (“superresolution”) localization of hundreds or thousands of copies of two differently labeled proteins in the same live cell. In typical experiments, fluorescence from the entire three-dimensional (3D) cell body is projected along the z-axis of the microscope to form a 2D image at the camera plane. For imaging of two different species, here denoted “red” and “green”, a significant biological question is the extent to which the red and green spatial distributions are positively correlated, anti-correlated, or uncorrelated. A commonly used statistic for assessing the degree of linear correlation between two image matrices R and G is the Pearson Correlation Coefficient (PCC). PCC should vary from –1 (perfect anti-correlation) to 0 (no linear correlation) to +1 (perfect positive correlation). However, in the special case of spherocylindrical bacterial cells such as E. coli or B. subtilis, we show that the PCC fails both qualitatively and quantitatively. PCC returns the same +1 value for 2D projections of distributions that are either perfectly correlated in 3D or completely uncorrelated in 3D. The PCC also systematically underestimates the degree of anti-correlation between the projections of two perfectly anti-correlated 3D distributions. The problem is that the projection of a random spatial distribution within the 3D spherocylinder is non-random in 2D, whereas PCC compares every matrix element of R or G with the constant mean value R or G. We propose a modified Pearson Correlation Coefficient (MPCC) that corrects this problem for spherocylindrical cell geometry by using the proper reference matrix for comparison with R and G. Correct behavior of MPCC is confirmed for a variety of numerical simulations and on experimental distributions of HU and RNA polymerase in live E. coli cells. The MPCC concept should be generalizable to other cell shapes.

scholarly journals STUDY OF INTRAABDOMINAL PRESSURE IN ACUTE PANCREATITIS AND ITS CORRELATION WITH DISEASE SEVERITY

2021 ◽  
pp. 1-3
Author(s):  
Nithin K B ◽  
Kaushalendra Kumar ◽  
B J Sharath Chandra
Keyword(s):  
Acute Pancreatitis ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Pearson Correlation ◽  
Scoring Systems ◽  
Intraabdominal Pressure ◽  
Apache Ii Score ◽  
Abdominal Pressure ◽  
Pearson Correlation Coefficient ◽  
Positive Linear Correlation

INTRODUCTION: Acute pancreatitis is one of the commonly encountered surgical conditions in the emergency department. Prognostication is done by various biochemical assays and scoring systems. Intra abdominal pressure (IAP) measurement has strong potential use as a prognostication and interventional marker. In this study we aim to determine the association between the intra abdominal pressures and the severity, morbidity, mortality and the prognosis of the patient. METHODS: This study is prospective observational study. 100 patients diagnosed with acute pancreatitis were observed from the day of admission and followed up to the day of discharge. Intra abdominal pressure to be measured by the intravesical method.. RESULTS : There was significant linear correlation between raised intra abdominal pressure (IAP) and duration of hospitalization. In groups of patients who suffered complications, IAP was noted to be significantly higher, compared to the group of patients with no complications. (p <0.0001)There was a significant correlation of the baseline IAP (IAP at admission) with the MCTSI (pearson correlation coefficient = 0.534), APACHE II score (Pearson correlation coefficient = 0.511). IAP also showed positive linear correlation with ranson score (Pearson correlation coefficient = 0.383) CONCLUSION: IAP measurement is cheap, easy, and minimally invasive modality. It can be easily measured in catheterized patients. It is faster and easier than the existing scoring modalities and can reliably predict hospital stay, complications, and the prognosis in acute pancreatitis.

The mutual-information-based variance–covariance approach: an application to operational risk aggregation in Chinese banking

2014 ◽  
Vol 9 (3) ◽  
pp. 3-19 ◽  
Author(s):  
Jianping Li ◽  
Xiaoqian Zhu ◽  
Yongjia Xie ◽  
Jianming Chen ◽  
Lijun Gao ◽  
...  
Keyword(s):  
Mutual Information ◽  
Operational Risk ◽  
Risk Aggregation ◽  
Chinese Banking

Identifying the Source of Weathered Petroleum: Matching Infrared Spectra with Correlation Coefficients

1977 ◽  
Vol 31 (6) ◽  
pp. 524-527 ◽  
Author(s):  
Carl D. Baer ◽  
Chris W. Brown
Keyword(s):  
Correlation Coefficient ◽  
Similarity Measure ◽  
Infrared Spectra ◽  
Nearest Neighbor ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pearson Correlation Coefficient ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Novel Method

A novel method is presented for identifying the source of weathered petroleum by measuring the similarity between the spectrum of a weathered oil and spectra of artificially weathered samples. The Pearson correlation coefficient is used as the similarity measure, and three separate K-nearest neighbor approaches are tested on the data set.

scholarly journals Strain-level analysis of microbial community and detection of mutually exclusive relationships uncover complexity of inflammatory bowel disease

2020 ◽  
Author(s):  
Sunguk Shin ◽  
Jihyun F. Kim
Keyword(s):  
Inflammatory Bowel Disease ◽  
Microbial Communities ◽  
16S Rdna ◽  
Correlation Coefficient ◽  
Bowel Disease ◽  
Pearson Correlation ◽  
Rrna Gene ◽  
Data Set ◽  
Intraspecies Variation ◽  
Inflammatory Bowel

Abstract Background Despite intraspecies variation in ecological interaction and cellular function, most analytical methods for microbial communities that use the 16S rRNA gene are employed at the taxonomic level. Further, methods that detect positive or negative relationships between microbes such as Pearson correlation coefficient are generally applicable for linear correlations. Results We present METATEA for the analysis of community members with identical 16S rDNA sequences to define intraspecies groups, and for the calculation of exclusive correlation coefficient (ECC) to detect mutually exclusive relationships. Proportional variation of identical sequence groups in each disease subtype was revealed using a 16S rDNA data set of inflammatory bowel disease (IBD) samples. Conclusions Results at the identical sequence level complied with and outperformed those at the species or genus levels. Intraspecies variation was prevalent within Faecalibacterium prausnitzii , suggesting that some strains might be associated with diseases although it is known to be abundant in the human gut microbiota of healthy adults. IBD samples were categorized into two groups based on the ratios of certain Bacteroides vulgatus -like sequences. ECC identified strains antagonistic to disease-associated bacteria, thus proving their potential as probiotics in precision medicine. Klebsiella pneumoniae showed exclusive relationships with various bacteria, and its proportion was associated with bacterial diversity and Crohn’s disease. We expect METATEA to allow high resolution analysis of microbial communities and easy identification of pathogen-antagonistic probiotic microbes.

The basics of determining the coefficients of a linear correlation

2020 ◽  
Vol 65 (6) ◽  
pp. 69-87 ◽  
Author(s):  
Zbigniew Śleszyński
Keyword(s):  
Correlation Coefficient ◽  
Linear Correlation ◽  
Partial Correlation ◽  
Least Squares Method ◽  
Pearson Correlation ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Multivariate Correlation ◽  
Mutual Relations ◽  
Partial Correlation Coefficient

The aim of the paper is to present the basic measures related to the analysis of relationships between quantitative variables used in econometric modelling and their selected applications. The following measures are discussed: the Pearson correlation coefficient, the multivariate correlation coefficient, coefficient of determination, partial correlation coefficient and semi-partial correlation coefficient. A homogeneous approach is applied to the measures presented. Each is defined as a linear correlation coefficient of relevant vectors derived from regression equations. Additionally, mutual relations between the coefficients are described. Bordered matrices have been applied to the calculations, which significantly simplified the process, while the Statistica 13.3 PL program was used to verify the correctness of the calculations. The issue is illustrated in the model of regression of salary growth in Poland in the years 2001–2019 with four covariates, estimated using the least squares method.

scholarly journals MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Luman Wang ◽  
Qiaochu Mo ◽  
Jianxin Wang
Keyword(s):  
Mutual Information ◽  
Immune Cells ◽  
Linear Correlation ◽  
Pearson Correlation ◽  
Correlation Method ◽  
Specific Gene ◽  
Nonlinear Correlation ◽  
Gene Coexpression ◽  
Human Genes ◽  
Cell Groups

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI andrvalues, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI,r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.

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