Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Forward Sufficient Dimension Reduction for Categorical and Ordinal
  Responses   We present a forward sufficient dimension reduction method for categorical or
ordinal responses by extending the outer product of gradients and minimum
average variance estimator to multinomial generalized linear model. Previous
work in this direction extend forward regression to binary responses, and are
applied in a pairwise manner to multinomial data, which is less efficient than
our approach. Like other forward regression-based sufficient dimension
reduction methods, our approach avoids the relatively stringent distributional
requirements necessary for inverse regression alternatives. We show consistency
of our proposed estimator and derive its convergence rate. We develop an
algorithm for our methods based on repeated applications of available
algorithms for forward regression. We also propose a clustering-based tuning
procedure to estimate the tuning parameters. The effectiveness of our estimator
and related algorithms is demonstrated via simulations and applications.

11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math