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Given the significant role high-stakes testing plays, it is important to examine how tests function and what they really measures.
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For this purpose we propose two novel sets of test functions known as real and imaginary discrepancy test function, and real and imaginary cross-validation test functions.
Therefore we are able to introduce a test function and a distribution space.
To obtain the reverse implication we use standard test function and Lemma 2.5 (42.5.
We will use the suitable test function and Gronwall's inequality to derive it.
From Definition 2.1, we have (2.2). with nonnegative test function and.
To this end, fix any test function and define the radially symmetric mapping such that.
For analysis of microarray data, Significance Analysis of Microarrays (SAM) using the Student's t-test function and data randomization to establish a False Discovery Rate (FDR) was used.
As a distribution, the Dirac delta is a linear functional on the space of test functions and is defined by \delta[\varphi] = \varphi(0)\, for every test function φ.
Test functions and experimentally measured cutting tool accelerations are examined.
Experiments were conducted on a set of traditional multimodal test functions and CEC 2013 benchmark functions.
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