Central limit theorems for high dimensional dependent data
Central limit theorems for high dimensional dependent data Page 1 Bernoulli 30(1), 2024,
712–742 https://doi.org/10.3150/23-BEJ1614 Central limit theorems for high dimensional …
712–742 https://doi.org/10.3150/23-BEJ1614 Central limit theorems for high dimensional …
[PDF][PDF] Inference of Grouped Time-Varying Network Vector Autoregression Models
D Li, B Peng, S Tang, W Wu - 2023 - monash.edu
This paper considers statistical inference of time-varying network vector autoregression
models for large-scale time series. A latent group structure is imposed on the heterogeneous …
models for large-scale time series. A latent group structure is imposed on the heterogeneous …
Precision Least Squares: Estimation and Inference in High-Dimensions
L Margaritella, R Sessinou - Journal of Business & Economic …, 2024 - Taylor & Francis
The least squares estimator can be cast as depending only on the precision matrix of the
data, similar to the weights of a global minimum variance portfolio. We give conditions under …
data, similar to the weights of a global minimum variance portfolio. We give conditions under …
Estimation of autocovariance matrices for high dimensional linear processes
K Furmańczyk - Metrika, 2021 - Springer
In this paper under some mild restrictions upper bounds on the rate of convergence for
estimators of p * pp× p autocovariance and precision matrices for high dimensional linear …
estimators of p * pp× p autocovariance and precision matrices for high dimensional linear …
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
J Mielniczuk - Entropy, 2022 - mdpi.com
The presented volume addresses some vital problems in contemporary statistical reasoning.
One of them is high dimensionality of the studied phenomenon and its consequences for …
One of them is high dimensionality of the studied phenomenon and its consequences for …