normalise: Transform the noise to be closer to the Gaussian distribution
In IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection

Description Usage Arguments Details Value Author(s) See Also Examples

This function pre-processes the given data in order to obtain a noise structure that is closer to satisfying the Gaussianity assumption. See details for more information and for the relevant literature reference.

1	normalise(x, sc = 3)

`x`	A numeric vector containing the data.
`sc`	A positive integer number with default value equal to 3. It is used to define the way we pre-average the given data sequence.

For a given natural number sc and data x of length T, let us denote by Q = \lceil T/sc \rceil. Then, normalise calculates

\tilde{x}_q = 1/sc∑_{t=(q-1) * sc + 1}^{q * sc}x_t,

for q=1, 2, ..., Q-1, while

\tilde{x}_Q = (T - (Q-1) * sc)^{-1}∑_{t = (Q-1) * sc + 1}^{T}x_t.

More details can be found in the preprint “Detecting multiple generalized change-points by isolating single ones”, Anastasiou and Fryzlewicz (2018).

The “normalised” vector \tilde{x} of length Q, as explained in Details.

Andreas Anastasiou, a.anastasiou@lse.ac.uk

ht_ID_pcm and ht_ID_cplm, which are functions that employ normalise.