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Recently Professor Simon Wren-Lewis on his must-read Mainly Macro blog posed an interesting symmetry test for the two I call the Irrelevant Minority (IM), given that the MPC is tasked with a symmetric target, of preventing inflation getting too far above or below the 2 per cent target.
Thus, although the Ueda's method is an outlier detection method, it does perform like a symmetry test in some cases.
Finally, to ensure the blindness of the algorithm, a symmetry test is checked on the estimated version of the CAF.
Given that the Ueda's method focuses on the symmetry of the distribution, this outlier detection method can also serve as a symmetry test.
To model the symmetry test, a symmetry index IND sym ( τ 0 ) is calculated by ignoring the first amplitude that corresponds to α = 0 and by measuring the mean value of the abscissa of the remaining l nonzero elements.
The goal of any symmetry test is to test the null hypothesis that the distribution of a random variable is symmetric about the centre of the distribution (see Hollander 1981).
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Thus, the Ueda's method should also be studied in the context of symmetry tests.
The goal is to prove large deviations limit theorems for statistics, which are based on kernel density estimator and which are designed for symmetry testing.
The goal of this paper is to provide large deviations limit theorems for statistics, based on the delta-sequence density estimation and designed to symmetry testing of distribution.
However, simulation studies are needed to compare the performance of this method against existent symmetry tests (Csörgo and Heathcote 1987; Randles et al. 1980; Yoshizawa 1984; Zheng and Gastwirth 2010) and thus determine whether the Ueda's method could also classify as a test of this sort.
Probe trials were mixed with baseline trials in the symmetry testing sessions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com