Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gap bootstrap methods for massive data sets with an application to
  transportation engineering   In this paper we describe two bootstrap methods for massive data sets. Naive
applications of common resampling methodology are often impractical for massive
data sets due to computational burden and due to complex patterns of
inhomogeneity. In contrast, the proposed methods exploit certain structural
properties of a large class of massive data sets to break up the original
problem into a set of simpler subproblems, solve each subproblem separately
where the data exhibit approximate uniformity and where computational
complexity can be reduced to a manageable level, and then combine the results
through certain analytical considerations. The validity of the proposed methods
is proved and their finite sample properties are studied through a moderately
large simulation study. The methodology is illustrated with a real data example
from Transportation Engineering, which motivated the development of the
proposed methods.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th