Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dimension reduction-based significance testing in nonparametric
  regression   A dimension reduction-based adaptive-to-model test is proposed for
significance of a subset of covariates in the context of a nonparametric
regression model. Unlike existing local smoothing significance tests, the new
test behaves like a local smoothing test as if the number of covariates were
just that under the null hypothesis and it can detect local alternatives
distinct from the null at the rate that is only related to the number of
covariates under the null hypothesis. Thus, the curse of dimensionality is
largely alleviated when nonparametric estimation is inevitably required. In the
cases where there are many insignificant covariates, the improvement of the new
test is very significant over existing local smoothing tests on the
significance level maintenance and power enhancement. Simulation studies and a
real data analysis are conducted to examine the finite sample performance of
the proposed test.

25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int