Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-dimensional Generalized Additive Mixed Model with Longitudinal Data   The problem of simultaneously estimating and selecting important variables is
challenging in the high-dimensional generalized additive mixed model (GAMM)
with longitudinal data because of the correlation structures. We give a
penalized estimation strategy for efficient variable selection when the number
of variables in the linear part and number of components in the non-linear part
can grow with the sample size. A Monte Carlo Newton-Raphson algorithm is
developed where a Metropolis step for generating random effects is used to
apply the proposed double-penalization technique in the high-dimensional GAMM.
In addition, to improve efficiency for regression coefficients, the estimation
of the working covariance matrix is involved in the proposed iterative
algorithm. We further study the asymptotic performance of the resulting
estimators and establish the oracle properties. We conduct extensive numerical
studies to assess the performance of our proposed estimation strategy and
numerically illustrate how efficient it is in selecting significant variables.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
1
The categories of this paper are: cs
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin