Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Pattern-Mixture Models for Inference with Missing Data   Pattern-mixture models provide a transparent approach for handling missing
data, where the full-data distribution is factorized in a way that explicitly
shows the parts that can be estimated from observed data alone, and the parts
that require identifying restrictions. We introduce a nonparametric estimator
of the full-data distribution based on the pattern-mixture model factorization.
Our approach uses the empirical observed-data distribution and augments it with
a nonparametric estimator of the missing-data distributions under a given
identifying restriction. Our results apply to a large class of donor-based
identifying restrictions that encompasses commonly used ones and can handle
both monotone and nonmonotone missingness. We propose a Monte Carlo procedure
to derive point estimates of functionals of interest, and the bootstrap to
construct confidence intervals.

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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org