DVV uses the predictability of a spatial series in a phase-space to identify linearity/nonlinearity within a data series using a surrogate data methodology to derive a test statistic.
Our goal is to derive a test information curve tuned for screening purposes.
However, the model implementation used these data to derive a test probability matrix separately describing the relationship between each health state (Normal; PCR HPV positive; CIN1, CIN2, CIN3+, etc).
To derive a test statistic for this, we will begin by making the simplifying but usually false assumption that no LD exists between variants.
Methods for EST analysis include the use of a Poisson model for the EST counts to derive a test statistic [ 15, 18], a multinomial model leading to a traditional Chi Squared test [ 19], or a test conditioned on a constant total EST count using a hypergeometric or binomial distribution [ 19].
The function b satisfies the inequalities (1 − 1 − qV ) / qV ≤ b (q, V ) ≤ qV = n / N 0. A more practical criterion, or rule of thumb, may be to derive a test statistic z with close to a standard normal distribution under the null hypothesis, and check whether z > n N 0 z α.
Fisher derived an exact test of the maximum periodogram coordinate by introducing the g-statistic: where I(ω k ) is a k-th peak of the periodogram.
We also derived an optimal test statistic to detect the signature of population reconnection from the SFS (Text A and Figure D in File S1) and compute the power of common neutrality tests to detect such events.
To robustly detect the excess of variants on the SFS generated by a reconnection event, we derived an optimal test statistic, denoted T Ω, using developments provided by Ferretti et al. (2010) and Achaz (2009) (File S1).
In the same way Costes and coworkers derived a statistical significance test to evaluate the significant correlation in a pair of images [3], we derived a statistical significance test to evaluate the probability (P) that the measured value of R from the two images is significantly greater than values of R that would be calculated if there was only random overlap.
