Inference for High-Dimensional Sparse Spectral Precision Matrices
Researchers have developed a new statistical framework for inferring conditional dependence structures in high-dimensional time series data. This method addresses challenges posed by discrete Fourier transforms, which introduce biases, and the complex-valued nature of spectral precision matrices. The proposed approach utilizes the full likelihood of neighboring discrete Fourier transforms to construct a debiased graphical lasso estimator, enabling more accurate inference and improved detection power. AI