ArticleJournal of the American Statistical Association2022
Mean and Covariance Estimation for Functional Snippets.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Forecasting the Regional Demand for Medical Workers in Kazakhstan: The Functional Principal Component Analysis Approach.International journal of environmental research and public health · 2025Article
- Article
- Testing homogeneity: the trouble with sparse functional data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
- Testing Linear Operator Constraints in Functional Response Regression with Incomplete Response Functions.Electronic journal of statistics · 2023Article
- Robust Inference for Partially Observed Functional Response Data.Statistica Sinica · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subinterval that is much shorter than the entire study interval. Estimation of the covariance function for functional snippets is challenging since information for the far off-diagonal regions of the covariance structure is completely missing. We address this difficulty by decomposing the covariance function into a variance function component and a correlation function component. The variance function can be effectively estimated nonparametrically, while the correlation part is modeled parametrically, possibly with an increasing number of parameters, to handle the missing information in the far off-diagonal regions. Both theoretical analysis and numerical simulations suggest that this hybrid strategy is effective. In addition, we propose a new estimator for the variance of measurement errors and analyze its asymptotic properties. This estimator is required for the estimation of the variance function from noisy measurements.
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