Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rank-based Estimation under Asymptotic Dependence and Independence, with
  Applications to Spatial Extremes   Multivariate extreme value theory is concerned with modeling the joint tail
behavior of several random variables. Existing work mostly focuses on
asymptotic dependence, where the probability of observing a large value in one
of the variables is of the same order as observing a large value in all
variables simultaneously. However, there is growing evidence that asymptotic
independence is equally important in real world applications. Available
statistical methodology in the latter setting is scarce and not well understood
theoretically. We revisit non-parametric estimation and introduce rank-based
M-estimators for parametric models that simultaneously work under asymptotic
dependence and asymptotic independence, without requiring prior knowledge on
which of the two regimes applies. Asymptotic normality of the proposed
estimators is established under weak regularity conditions. We further show how
bivariate estimators can be leveraged to obtain parametric estimators in
spatial tail models, and again provide a thorough theoretical justification for
our approach.

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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cs
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The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph