Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A maximum-mean-discrepancy goodness-of-fit test for censored data   We introduce a kernel-based goodness-of-fit test for censored data, where
observations may be missing in random time intervals: a common occurrence in
clinical trials and industrial life-testing. The test statistic is
straightforward to compute, as is the test threshold, and we establish
consistency under the null. Unlike earlier approaches such as the Log-rank
test, we make no assumptions as to how the data distribution might differ from
the null, and our test has power against a very rich class of alternatives. In
experiments, our test outperforms competing approaches for periodic and Weibull
hazard functions (where risks are time dependent), and does not show the
failure modes of tests that rely on user-defined features. Moreover, in cases
where classical tests are provably most powerful, our test performs almost as
well, while being more general.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th