incf-nidash/nidm — pull request #176 by cmaumet (2014-09-18 10:10:56+00:00)
Title: Noise models and estimation methods

This is a proposition built with @nicholst to represent the noise models (following suggestion in #170).

We propose to characterise the noise variance (diagonal elements) and the noise dependance (off-diagonal elements):

**Noise Variance**
- is
- homogeneous if all variances are identical (`nidm:noiseVarianceHomogeneous = true`) or;
- heterogeneous otherwise (`nidm:noiseVarianceHomogeneous = false`).
- Heterogeneous variance can be further specified (e.g. by group, by time, FSL FLAME, arbitrary...) using a separate attribute
- is defined spatially to be either:
- local (`nidm:varianceSpatialModel = nidm:SpatiallyLocal`) or;
- regularised (`nidm:varianceSpatialModel = nidm:SpatiallyRegularised`) or;
- global (`nidm:varianceSpatialModel = nidm:SpatiallyGlobal`).

**Noise Dependence**
- is either
- Independent (`nidm:noiseDependence = nidm:IndependentNoise`)
- Compound Symmetry (`nidm:noiseDependence = nidm:CompoundSymmetry`)
- Serially Correlated (`nidm:noiseDependence = nidm:SeriallyCorrelatedNoise`)
- SeriallyCorrelated noise can be ApproxAR1, ARMA11, TaperedACF
- Arbitrarily Correlated (`nidm:noiseDependence = nidm:AbitrarilyCorrelatedNoise`)
- is defined spatially to be either:
- local (`nidm:DependenceSpatialModel = nidm:SpatiallyLocal`) or;
- regularised (`nidm:DependenceSpatialModel = nidm:SpatiallyRegularised`) or;
- global (`nidm:DependenceSpatialModel = nidm:SpatiallyGlobal`).