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A Robust Approach to ARMA Factor Modeling
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DOI：10.1109/TAC.2023.3274710.png)
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摘要
En 中文
This article deals with the dynamic factor analysis problem for an ARMA process. To robustly estimate the number of factors, we construct a confidence region centered in a finite sample estimate of the underlying model, which contains the true model with a prescribed probability. In this confidence region, the problem, formulated as a rank minimization of a suitable spectral density, is efficiently approximated via a trace norm convex relaxation. The latter is addressed by resorting to the Lagrange duality theory, which allows to prove the existence of solutions. Finally, a numerical algorithm to solve the dual problem is presented. The effectiveness of the proposed estimator is assessed through simulation studies both with synthetic and real data.
Keyword:
Convex optimization
duality theory
dynamic factor analysis (DFA)
nuclear norm
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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