Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Choice and Diagnostics for Linear Mixed-Effects Models Using
  Statistics on Street Corners   The complexity of linear mixed-effects (LME) models means that traditional
diagnostics are rendered less effective. This is due to a breakdown of
asymptotic results, boundary issues, and visible patterns in residual plots
that are introduced by the model fitting process. Some of these issues are well
known and adjustments have been proposed. Working with LME models typically
requires that the analyst keeps track of all the special circumstances that may
arise. In this paper we illustrate a simpler but generally applicable approach
to diagnosing LME models. We explain how to use new visual inference methods
for these purposes. The approach provides a unified framework for diagnosing
LME fits and for model selection. We illustrate the use of this approach on
several commonly available data sets. A large-scale Amazon Turk study was used
to validate the methods. R code is provided for the analyses.

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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: cs
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: physics