Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GLMMLasso: An Algorithm for High-Dimensional Generalized Linear Mixed
  Models Using L1-Penalization   We propose an L1-penalized algorithm for fitting high-dimensional generalized
linear mixed models. Generalized linear mixed models (GLMMs) can be viewed as
an extension of generalized linear models for clustered observations. This
Lasso-type approach for GLMMs should be mainly used as variable screening
method to reduce the number of variables below the sample size. We then suggest
a refitting by maximum likelihood based on the selected variables only. This is
an effective correction to overcome problems stemming from the variable
screening procedure which are more severe with GLMMs. We illustrate the
performance of our algorithm on simulated as well as on real data examples.
Supplemental materials are available online and the algorithm is implemented in
the R package glmmixedlasso.

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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg