Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient ANOVA for directional data   In this paper we tackle the ANOVA problem for directional data (with
particular emphasis on geological data) by having recourse to the Le Cam
methodology usually reserved for linear multivariate analysis. We construct
locally and asymptotically most stringent parametric tests for ANOVA for
directional data within the class of rotationally symmetric distributions. We
turn these parametric tests into semi-parametric ones by (i) using a
studentization argument (which leads to what we call pseudo-FvML tests) and by
(ii) resorting to the invariance principle (which leads to efficient rank-based
tests). Within each construction the semi-parametric tests inherit optimality
under a given distribution (the FvML distribution in the first case, any
rotationally symmetric distribution in the second) from their parametric
antecedents and also improve on the latter by being valid under the whole class
of rotationally symmetric distributions. Asymptotic relative efficiencies are
calculated and the finite-sample behavior of the proposed tests is investigated
by means of a Monte Carlo simulation. We conclude by applying our findings on a
real-data example involving geological data.

11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph