Comparison of Randomization method for RV coefficient and Permutation Method for Hotelling T-Squared

2014 ◽  
Vol 2014 (2) ◽  
pp. 37-73
Author(s):  
Charles Aronu ◽  
◽  
Godday Ebuh ◽  
Cecilia Okoli ◽  
Rose Nwosu ◽  
...  
Keyword(s):  
Rv Coefficient ◽  
Randomization Method

scholarly journals Max-Min Processors Scheduling

2021 ◽  
Vol 50 (1) ◽  
pp. 5-12
Author(s):  
Hani Alquhayz ◽  
Mahdi Jemmali
Keyword(s):  
Literature Review ◽  
Knapsack Problem ◽  
Completion Time ◽  
Research Work ◽  
Parallel Processors ◽  
Experimental Results ◽  
Running Time ◽  
Similar Performance ◽  
Minimum Completion Time ◽  
Randomization Method

This paper focuses on the maximization of the minimum completion time on identical parallel processors. The objective of this maximization is to ensure fair distribution. Let a set of jobs to be assigned to several identical parallel processors. This problem is shown as NP-hard. The research work of this paper is based essentially on the comparison of the proposed heuristics with others cited in the literature review. Our heuristics are developed using essentially the randomization method and the iterative utilization of the knapsack problem to solve the studied problem. Heuristics are assessed by several instances represented in the experimental results. The results show that the knapsack based heuristic gives almost a similar performance than heuristic in a literature review but in better running time.  

Global sensitivity analysis of statistical models by double randomization method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Dmitriy Kolyukhin
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Models ◽  
Global Sensitivity Analysis ◽  
Uncertain Parameters ◽  
Suggested Approach ◽  
Global Sensitivity ◽  
Double Randomization Method ◽  
Complex Models ◽  
Monte Carlo Estimation ◽  
Randomization Method

Abstract The paper addresses a global sensitivity analysis of complex models. The work presents a generalization of the hierarchical statistical models where uncertain parameters determine the distribution of statistical models. The double randomization method is applied to increase the efficiency of the Monte Carlo estimation of Sobol indices. Numerical computations are provided to study the accuracy and efficiency of the proposed technique. The issue of optimization of the suggested approach is considered.

scholarly journals Modeling of the Distribution of Heavy Metals and Trace Elements in Argan Forest Soil and Parts of Argan Tree

2013 ◽  
Vol 8 (1) ◽  
pp. 1934578X1300800 ◽  
Author(s):  
Faez A. E. Mohammed ◽  
Rahma Bchitou ◽  
Mohamed Boulmane ◽  
Ahmed Bouhaouss ◽  
Dominique Guillaume
Keyword(s):  
Heavy Metals ◽  
Trace Elements ◽  
Forest Soil ◽  
Multidimensional Analysis ◽  
Soil Group ◽  
Argan Tree ◽  
Calcium Phosphorus ◽  
Copper Zinc ◽  
Rv Coefficient ◽  
Metals And Trace Elements

The transfer of heavy metals and trace elements from argan forest soil into the wood, leaves, almonds, and argan oil was studied. Analyzed metals were: chromium, cadmium, copper, zinc, lead, calcium, phosphorus, potassium, and magnesium. Correlations linking different behaviors of the studied heavy metals and trace elements observed by multidimensional analysis were attributed to partial-spatial variations. Whereas the RV-coefficient of wood, leaf, almond and oil groups was high, the soil group correlated poorly with the other groups.

Synthetic Taxonomy of Rosa Races Using ACT-STATIS

2000 ◽  
Vol 55 (5-6) ◽  
pp. 399-409 ◽  
Author(s):  
Olivier Raymond ◽  
Jean-Louis Fiasson ◽  
Maurice Jay
Keyword(s):  
Artificial Selection ◽  
Contingency Tables ◽  
Principal Component ◽  
Data Sets ◽  
Independent Data ◽  
Selection Pressures ◽  
Principal Component Analyses ◽  
History Of ◽  
Rv Coefficient ◽  
Good Agreement

Fifteen Rosa cultivated races were described by means of phenotypic frequencies (11 tables). Two groups of correlated contingency tables were identified by ACT-STATIS (Analyse Conjointe de Tableaux - Structuration de Tableaux à Trois Indices de la Statistique) interstructure analysis. Three data sets appeared to be independent from the others. Typologies of races were obtained after ACT-STATIS compromise analyses for the two groups of correlated tables, and after Principal Component Analyses for the independent data sets. Each typology was original and variously influenced by genealogical structure, mutation or artificial selection pressures. A weighted synthesis was attempted in order to build a taxonomy of races taking into account these diversity factors. The good agreement between the resulting classification and the assumptions about the history of Rosa domestication advocated for a wider utilization of ACT-STATIS and RV coefficient when the relationships between individuals or populations have to be studied on the basis of their similarities.

scholarly journals Another look at matrix correlations

2019 ◽  
Vol 35 (22) ◽  
pp. 4748-4753 ◽  
Author(s):  
Ahmad Borzou ◽  
Razie Yousefi ◽  
Rovshan G Sadygov
Keyword(s):  
Matrix Decomposition ◽  
Data Matrix ◽  
Supplementary Information ◽  
Biological Processes ◽  
Single Experiment ◽  
Sample Number ◽  
The Matrix ◽  
Rv Coefficient ◽  
Systems View ◽  
Data Matrices

Abstract Motivation High throughput technologies are widely employed in modern biomedical research. They yield measurements of a large number of biomolecules in a single experiment. The number of experiments usually is much smaller than the number of measurements in each experiment. The simultaneous measurements of biomolecules provide a basis for a comprehensive, systems view for describing relevant biological processes. Often it is necessary to determine correlations between the data matrices under different conditions or pathways. However, the techniques for analyzing the data with a low number of samples for possible correlations within or between conditions are still in development. Earlier developed correlative measures, such as the RV coefficient, use the trace of the product of data matrices as the most relevant characteristic. However, a recent study has shown that the RV coefficient consistently overestimates the correlations in the case of low sample numbers. To correct for this bias, it was suggested to discard the diagonal elements of the outer products of each data matrix. In this work, a principled approach based on the matrix decomposition generates three trace-independent parts for every matrix. These components are unique, and they are used to determine different aspects of correlations between the original datasets. Results Simulations show that the decomposition results in the removal of high correlation bias and the dependence on the sample number intrinsic to the RV coefficient. We then use the correlations to analyze a real proteomics dataset. Availability and implementation The python code can be downloaded from http://dynamic-proteome.utmb.edu/MatrixCorrelations.aspx. Supplementary information Supplementary data are available at Bioinformatics online.

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