Fisher Scoring for crossed factor linear mixed models

@article{MaullinSapey2021FisherSF,
  title={Fisher Scoring for crossed factor linear mixed models},
  author={Thomas Maullin-Sapey and Thomas E. Nichols},
  journal={Statistics and Computing},
  year={2021},
  volume={31}
}
The analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed for such purposes. Historically, a large proportion of material published on the LMM concerns the application of popular numerical optimization algorithms, such as Newton–Raphson, Fisher Scoring and expectation maximization to single-factor LMMs (i.e. LMMs that… 
1 Citations
BLMM: Parallelised Computing for Big Linear Mixed Models
TLDR
The “Big” Linear Mixed Models (BLMM) toolbox is described, an efficient Python package for large-scale fMRI LMM analyses designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in the previous work.

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