Statistical Inference for High-Dimensional Matrix-Variate Factor Models
نویسندگان
چکیده
This paper considers the estimation and inference of low-rank components in high-dimensional matrix-variate factor models, where each dimension matrix-variates ($p \times q$) is comparable to or greater than number observations ($T$). We propose an method called $\alpha$-PCA that preserves matrix structure aggregates mean contemporary covariance through a hyper-parameter $\alpha$. develop inferential theory, establishing consistency, rate convergence, limiting distributions, under general conditions allow for correlations across time, rows, columns noise. show both theoretical empirical methods choosing best $\alpha$, depending on use-case criteria. Simulation results demonstrate adequacy asymptotic approximating finite sample properties. The compares favorably with existing ones. Finally, we illustrate its applications real numeric data set two image sets. In all applications, proposed procedure outperforms previous power variance explanation using out-of-sample 10-fold cross-validation.
منابع مشابه
Statistical Inference for High Dimensional Data
STATISTICAL INFERENCE FOR HIGH DIMENSIONAL DATA
متن کاملStatistical inference in high dimensional linear and AFT models
A large amount of previous literature proposed and studied variable selection procedures for high dimensional data, and most of the researchers focused on the selection properties as well as the point estimation properties. However, there have been limited studies considering the construction of confidence intervals for the highdimensional variable selection problems. In this thesis, we propose...
متن کاملDynamic Matrix-Variate Graphical Models
This paper introduces a novel class of Bayesian models for multivariate time series analysis based on a synthesis of dynamic linear models and graphical models. The synthesis uses sparse graphical modelling ideas to introduce structured, conditional independence relationships in the time-varying, cross-sectional covariance matrices of multiple time series. We define this new class of models and...
متن کاملPredictive Matrix-Variate t Models
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn from a matrix-variate t distribution and suggest a matrixvariate tmodel (MVTM) to predict those missing elements. We show that MVTM generalizes a range of known probabilistic models, and automatically performs model se...
متن کاملInference for high-dimensional sparse econometric models
This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is wellapproximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regressi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2021
ISSN: ['0162-1459', '1537-274X', '2326-6228', '1522-5445']
DOI: https://doi.org/10.1080/01621459.2021.1970569