Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confronting Quasi-Separation in Logistic Mixed Effects for Linguistic
  Data: A Bayesian Approach   Mixed effects regression models are widely used by language researchers.
However, these regressions are implemented with an algorithm which may not
converge on a solution. While convergence issues in linear mixed effects models
can often be addressed with careful experiment design and model building,
logistic mixed effects models introduce the possibility of separation or
quasi-separation, which can cause problems for model estimation that result in
convergence errors or in unreasonable model estimates. These problems cannot be
solved by experiment or model design. In this paper, we discuss
(quasi-)separation with the language researcher in mind, explaining what it is,
how it causes problems for model estimation, and why it can be expected in
linguistic datasets. Using real linguistic datasets, we then show how Bayesian
models can be used to overcome convergence issues introduced by
quasi-separation, whereas frequentist approaches fail. On the basis of these
demonstrations, we advocate for the adoption of Bayesian models as a practical
solution to dealing with convergence issues when modeling binary linguistic
data.

5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio