cosanlab/nltools

Improve auto-correlation estimation during regression

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#307 opened on Jul 22, 2019

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enhancementhelp wanted

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Description

Currently, we do a pretty crude ARMA model if the user wants to model auto-correlations. We have a few options to improve this:

  1. AFNI style: implement our own limited grid search of ARMA parameters per voxel rather than unconstrained optimization in statsmodels (currrent) - probably the trickiest and slowest
  2. Implement pre-whitening routines by first estimating OLS, then using the residuals of order N to adjust the data, and finally fit OLS again. Nistats has an implentation of this: see this and this.
  3. Port over SPMs new FAST routine which does something like using basis-sets of covariance matrices to account for auto-correlation

Contributor guide

Research direction
Study the current ARMA implementation in the codebase, then explore the three mentioned improvement options: 1) AFNI style grid search, 2) Nistats pre whitening, 3) FAST routine. Evaluate the computational complexity and accuracy of each approach for neuroimaging time series data.
Tech stack
python
Domain
data
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonStatistics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
45