Year: 2020
Type: article
Abstract: This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision matrix. To accomplish this purpose, we establish an abstract asymptotic theory for the weighted graphical Lasso and its de-biased version without specifying the form of the initial covariance estimator. We also extend the scope of the theory to the case that a known factor structure is present in the data. The developed theory is applied to the concrete situation where we can use the realized covariance matrix as the initial covariance estimator, and we obtain a feasible asymptotic distribution theory to construct (simultaneous) confidence intervals and (multiple) testing procedures for entries of the precision matrix.
Source: Entropy
DOI: 10.3390/e22040456
Author: Yuta Koike
Institution: The University of Tokyo
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,690
Awards: Interdisciplinary research in mathematical/data science and simulation science for stochastic differential equation models, Statistical analysis of high-dimensional high-frequency data, G7440878833, New developments of statistical methods for risk management in finance and insurance, G8375348420
OpenAlex ID: W2943068099