Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effect Size Estimation and Misclassification Rate Based Variable
  Selection in Linear Discriminant Analysis   Supervised classifying of biological samples based on genetic information,
(e.g. gene expression profiles) is an important problem in biostatistics. In
order to find both accurate and interpretable classification rules variable
selection is indispensable. This article explores how an assessment of the
individual importance of variables (effect size estimation) can be used to
perform variable selection. I review recent effect size estimation approaches
in the context of linear discriminant analysis (LDA) and propose a new
conceptually simple effect size estimation method which is at the same time
computationally efficient. I then show how to use effect sizes to perform
variable selection based on the misclassification rate which is the data
independent expectation of the prediction error. Simulation studies and real
data analyses illustrate that the proposed effect size estimation and variable
selection methods are competitive. Particularly, they lead to both compact and
interpretable feature sets.

19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph