Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized XGBoost Method   The XGBoost method has many advantages and is especially suitable for
statistical analysis of big data, but its loss function is limited to convex
functions. In many specific applications, a nonconvex loss function would be
preferable. In this paper, I propose a generalized XGBoost method, which
requires weaker loss function constraint and involves more general loss
functions, including convex loss functions and some non-convex loss functions.
Furthermore, this generalized XGBoost method is extended to multivariate loss
function to form a more generalized XGBoost method. This method is a
multiobjective parameter regularized tree boosting method, which can model
multiple parameters in most of the frequently-used parametric probability
distributions to be fitted by predictor variables. Meanwhile, the related
algorithms and some examples in non-life insurance pricing are given.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol