Analysis of Partial Rank Correlation Measures Based on the Product-Moment Model: Part One

Social Forces ◽  
1974 ◽  
Vol 53 (2) ◽  
pp. 229 ◽  
Author(s):  
Robert H. Somers
Keyword(s):  
Rank Correlation ◽  
Correlation Measures ◽  
Partial Rank Correlation

Note on Use of phi as a Simplified Partial Rank Correlation Coefficient

1966 ◽  
Vol 18 (3) ◽  
pp. 973-974 ◽  
Author(s):  
Richard W. Johnson
Keyword(s):  
Correlation Coefficient ◽  
Rank Correlation ◽  
Partial Rank Correlation

Use of phi as a simplified partial rank correlation coefficient is described and illustrated.

Partial Rank Correlation

Biometrika ◽  
1942 ◽  
Vol 32 (3/4) ◽  
pp. 277 ◽  
Author(s):  
M. G. Kendall
Keyword(s):  
Rank Correlation ◽  
Partial Rank Correlation

Portfolio Optimization using Rank Correlation

2014 ◽  
pp. 1866-1879
Author(s):  
Chanaka Edirisinghe ◽  
Wenjun Zhou
Keyword(s):  
Portfolio Optimization ◽  
Rank Correlation ◽  
Financial Assets ◽  
Risk Averse ◽  
Out Of Sample ◽  
Return Distributions ◽  
Sample Testing ◽  
Correlation Measures ◽  
Computational Properties ◽  
Critical Challenge

A critical challenge in managing quantitative funds is the computation of volatilities and correlations of the underlying financial assets. We present a study of Kendall's t coefficient, one of the best-known rank-based correlation measures, for computing the portfolio risk. Incorporating within risk-averse portfolio optimization, we show empirically that this correlation measure outperforms that of Pearson's in our out-of-sample testing with real-world financial data. This phenomenon is mainly due to the fat-tailed nature of stock return distributions. We also discuss computational properties of Kendall's t, and describe efficient procedures for incremental and one-time computation of Kendall's rank correlation.

scholarly journals Quantifying the dose-dependent impact of intracellular amyloid beta in a mathematical model of calcium regulation in xenopus oocyte

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246116
Author(s):  
Joseph Minicucci ◽  
Molly Alfond ◽  
Angelo Demuro ◽  
David Gerberry ◽  
Joe Latulippe
Keyword(s):  
Mathematical Model ◽  
Amyloid Beta ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Dependent Manner ◽  
Partial Rank Correlation ◽  
Dose Dependent ◽  
Detrimental Impact ◽  
Oligomeric Forms

Alzheimer’s disease (AD) is a devastating illness affecting over 40 million people worldwide. Intraneuronal rise of amyloid beta in its oligomeric forms (iAβOs), has been linked to the pathogenesis of AD by disrupting cytosolic Ca2+ homeostasis. However, the specific mechanisms of action are still under debate and intense effort is ongoing to improve our understanding of the crucial steps involved in the mechanisms of AβOs toxicity. We report the development of a mathematical model describing a proposed mechanism by which stimulation of Phospholipase C (PLC) by iAβO, triggers production of IP3 with consequent abnormal release of Ca2+ from the endoplasmic reticulum (ER) through activation of IP3 receptor (IP3R) Ca2+ channels. After validating the model using experimental data, we quantify the effects of intracellular rise in iAβOs on model solutions. Our model validates a dose-dependent influence of iAβOs on IP3-mediated Ca2+ signaling. We investigate Ca2+ signaling patterns for small and large iAβOs doses and study the role of various parameters on Ca2+ signals. Uncertainty quantification and partial rank correlation coefficients are used to better understand how the model behaves under various parameter regimes. Our model predicts that iAβO alter IP3R sensitivity to IP3 for large doses. Our analysis also shows that the upstream production of IP3 can influence Aβ-driven solution patterns in a dose-dependent manner. Model results illustrate and confirm the detrimental impact of iAβOs on IP3 signaling.

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