scholarly journals Fast covariance estimation for multivariate sparse functional data

Stat ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Cai Li ◽  
Luo Xiao ◽  
Sheng Luo
Keyword(s):  
Functional Data ◽  
Covariance Estimation ◽  
Sparse Functional Data

scholarly journals Fast covariance estimation for sparse functional data

2017 ◽  
Vol 28 (3) ◽  
pp. 511-522 ◽  
Author(s):  
Luo Xiao ◽  
Cai Li ◽  
William Checkley ◽  
Ciprian Crainiceanu
Keyword(s):  
Functional Data ◽  
Covariance Estimation ◽  
Sparse Functional Data

scholarly journals Erratum to: Fast covariance estimation for sparse functional data

2017 ◽  
Vol 28 (3) ◽  
pp. 523-523 ◽  
Author(s):  
Luo Xiao ◽  
Cai Li ◽  
William Checkley ◽  
Ciprian Crainiceanu
Keyword(s):  
Functional Data ◽  
Covariance Estimation ◽  
Sparse Functional Data

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.

scholarly journals A notion of depth for sparse functional data

Test ◽  
2020 ◽  
Author(s):  
Carlo Sguera ◽  
Sara López-Pintado
Keyword(s):  
Functional Data ◽  
Sparse Functional Data

scholarly journals A Bayesian Model for Sparse Functional Data

Biometrics ◽  
2007 ◽  
Vol 64 (1) ◽  
pp. 54-63 ◽  
Author(s):  
Wesley K. Thompson ◽  
Ori Rosen
Keyword(s):  
Functional Data ◽  
Bayesian Model ◽  
Sparse Functional Data

scholarly journals Joint modelling of paired sparse functional data using principal components

Biometrika ◽  
2008 ◽  
Vol 95 (3) ◽  
pp. 601-619 ◽  
Author(s):  
L. Zhou ◽  
J. Z. Huang ◽  
R. J. Carroll
Keyword(s):  
Principal Components ◽  
Functional Data ◽  
Joint Modelling ◽  
Sparse Functional Data
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