MultiTestH0

a matrix whose colummns are tested to
 have zero mean or not.

proj.matrix

a numeric providing the known variance

data.var

a numeric vector of thresholds specified as
 the \(1-\alpha\) quantiles of the multiple test under
 the null on each partition. If thrs=NULL (default) return
 the global p-value which is the minimum of all p-values
 obtained on each partition.

thrs

MultiTestH0 tests if each column vectors of a matrix seen
as a noisy dynamic is of zero mean (\(H_0\)) or not. The
multiple statistical test assumes known variance and is
based on a multiple \(\chi^2\) test.

A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.

MultiTestH0: Statistical test of zero mean for dynamics

Description

Usage

Arguments

Value

References