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Their work shows that likelihood-based methods such as the likelihood ratio (LR) test, score test, and tests based on the method of generalized statistics developed by Weerahandi [ 6], provide efficient procedures for comparing coefficient of variations (CV) in univariate normal populations or from independent samples.
Here we mention tests based on heterozygosity, tests based on FST, tests based on Allele frequency, and tests based on haplotype.
Resolution properties of B-spline and compact finite difference schemes are compared using Fourier analysis in periodic domains, and tests based on solution of the wave and heat equations in finite domains, with uniform and nonuniform grids.
These include phenotypic culture based methods, tests for gene mutations and tests based on bacteriophage replication.
Comparable numbers of studies focused on imaging tests and tests based on a laboratory technique: 33% and 36%, respectively.
Besides these methods, selection can be detected using Poisson random-field models [ 28- 30], and tests based on linkage disequilibrium, haplotype structure and population differentiation [ 31- 36].
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In addition, a large panel of machine learning and data mining algorithms can be used, trained and tested based on this assembled dataset.
Then several decision tree-based classifiers are trained and tested based on these features.
Samples were fabricated and tested based on the known standards.
An initial SISO controller was developed and tested, based on a Simulink model of the process.
Two units have been built and tested based on solid state materials.
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