Multi-Horizon Mean-Covariance Estimation for Serial Correlated Returns

2019 ◽  
Author(s):  
Zhuanxin Ding
Keyword(s):  
Covariance Estimation

Structured Robust Covariance Estimation

2015 ◽  
Author(s):  
Ami Wiesel ◽  
Teng Zhang
Keyword(s):  
Covariance Estimation

Semi-Blind Channel Covariance Estimation for MIMO Precoding

2011 ◽  
Vol E94-A (2) ◽  
pp. 833-837
Author(s):  
Mohd Hairi HALMI ◽  
Mohd Yusoff ALIAS ◽  
Teong Chee CHUAH
Keyword(s):  
Covariance Estimation ◽  
Blind Channel ◽  
Mimo Precoding

The Brownian bridge E-M algorithm for covariance estimation with missing data

1998 ◽  
Vol 2 (2) ◽  
pp. 75-100 ◽  
Author(s):  
William Morokoff
Keyword(s):  
Missing Data ◽  
Brownian Bridge ◽  
Covariance Estimation

scholarly journals Covariance estimation via fiducial inference

2021 ◽  
pp. 1-16
Author(s):  
W. Jenny Shi ◽  
Jan Hannig ◽  
Randy C. S. Lai ◽  
Thomas C. M. Lee
Keyword(s):  
Covariance Estimation ◽  
Fiducial Inference

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

Deep Learning Based Downlink Channel Covariance Estimation for FDD Massive MIMO Systems

2021 ◽  
pp. 1-1
Author(s):  
Zou Linfu ◽  
Pan Zhiwen ◽  
Jiang Huilin ◽  
Liu Nan ◽  
You Xiaohu
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Covariance Estimation

Spectral-domain covariance estimation with a priori knowledge

2006 ◽  
Vol 42 (3) ◽  
pp. 1010-1020 ◽  
Author(s):  
P.R. Gurram ◽  
N.A. Goodman
Keyword(s):  
A Priori ◽  
Covariance Estimation ◽  
Spectral Domain ◽  
A Priori Knowledge ◽  
Priori Knowledge

scholarly journals An algorithm for the multivariate group lasso with covariance estimation

2017 ◽  
Vol 45 (4) ◽  
pp. 668-681 ◽  
Author(s):  
I. Wilms ◽  
C. Croux
Keyword(s):  
Group Lasso ◽  
Covariance Estimation

scholarly journals CELLO: A fast algorithm for Covariance Estimation

2013 ◽  
Author(s):  
William Vega-Brown ◽  
Abraham Bachrach ◽  
Adam Bry ◽  
Jonathan Kelly ◽  
Nicholas Roy
Keyword(s):  
Fast Algorithm ◽  
Covariance Estimation
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