Supplementary Appendix to Beta-Adjusted Covariance Estimation

2021 ◽  
Author(s):  
Kris Boudt ◽  
Kirill Dragun ◽  
Orimar Sauri ◽  
Steven Vanduffel
Keyword(s):  
Covariance Estimation ◽  
Supplementary Appendix

Structured Robust Covariance Estimation

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

Mining for Oil Forecasts: Supplementary Appendix

2020 ◽  
Author(s):  
Charles W. Calomiris ◽  
Nida Çakır Melek ◽  
Harry Mamaysky
Keyword(s):  
Supplementary Appendix

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.

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