Non-Parametric Analysis of Covariance

Biometrics ◽  
1995 ◽  
Vol 51 (3) ◽  
pp. 920 ◽  
Author(s):  
Stuart G. Young ◽  
Adrian W. Bowman
Keyword(s):  
Parametric Analysis ◽  
Analysis Of Covariance ◽  
Non Parametric

scholarly journals Non-parametric Analysis of Covariance ? The Case of Inhomogeneous and Heteroscedastic Noise

2007 ◽  
Vol 34 (3) ◽  
pp. 511-534 ◽  
Author(s):  
AXEL MUNK ◽  
NATALIE NEUMEYER ◽  
ACHIM SCHOLZ
Keyword(s):  
Parametric Analysis ◽  
Analysis Of Covariance ◽  
Non Parametric

Evaluating the Performance of Hedge Fund Strategies: A Non-Parametric Analysis

2015 ◽  
Author(s):  
Alessandra Canepa ◽  
Maria O. Gonzalez ◽  
Frank S. Skinner
Keyword(s):  
Parametric Analysis ◽  
Hedge Fund ◽  
Non Parametric

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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