Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable Prioritization in Nonlinear Black Box Methods: A Genetic
  Association Case Study   The central aim in this paper is to address variable selection questions in
nonlinear and nonparametric regression. Motivated by statistical genetics,
where nonlinear interactions are of particular interest, we introduce a novel
and interpretable way to summarize the relative importance of predictor
variables. Methodologically, we develop the "RelATive cEntrality" (RATE)
measure to prioritize candidate genetic variants that are not just marginally
important, but whose associations also stem from significant covarying
relationships with other variants in the data. We illustrate RATE through
Bayesian Gaussian process regression, but the methodological innovations apply
to other "black box" methods. It is known that nonlinear models often exhibit
greater predictive accuracy than linear models, particularly for phenotypes
generated by complex genetic architectures. With detailed simulations and two
real data association mapping studies, we show that applying RATE enables an
explanation for this improved performance.

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The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con