Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the penalized maximum likelihood estimation of high-dimensional
  approximate factor model   In this paper, we mainly focus on the penalized maximum likelihood estimation
(MLE) of the high-dimensional approximate factor model. Since the current
estimation procedure can not guarantee the positive definiteness of the error
covariance matrix, by reformulating the estimation of error covariance matrix
and based on the lagrangian duality, we propose an accelerated proximal
gradient (APG) algorithm to give a positive definite estimate of the error
covariance matrix. Combined the APG algorithm with EM method, a new estimation
procedure is proposed to estimate the high-dimensional approximate factor
model. The new method not only gives positive definite estimate of error
covariance matrix but also improves the efficiency of estimation for the
high-dimensional approximate factor model. Although the proposed algorithm can
not guarantee a global unique solution, it enjoys a desirable non-increasing
property. The efficiency of the new algorithm on estimation and forecasting is
also investigated via simulation and real data analysis.

33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ