Evidence map›Paper›PMID 26903705›Full record

ArticleStatistics and computing2016

Fast Covariance Estimation for High-dimensional Functional Data.

Luo Xiao, Vadim Zipunnikov, David Ruppert, Ciprian Crainiceanu

Abstract read
In one paragraph

Article in Statistics and computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

  1. Trial
  2. Article
  3. Contrastive Latent Functional Model.Statistics in biosciences · 2026
    Article
  4. Fast Bayesian Functional Principal Components Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026
    Article
  5. Article
  6. Functional quantile principal component analysis.Biostatistics (Oxford, England) · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows.Journal of agricultural, biological, and environmental statistics · 2025
    Article
  13. Article
  14. Functional support vector machine.Biostatistics (Oxford, England) · 2024
    Article
  15. Article
  16. Article
  17. Comparing Social Media Communities using Functional Data Analysis.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2024
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Luo XiaoDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD.
Vadim ZipunnikovDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD.
David RuppertDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD.
Ciprian CrainiceanuDepartment of Statistical Science and School of Operations Research and Information Engineering, Cornell University, Ithaca, NY.

Funding

Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
Statistical methods for large n and p problemsR01EB012547 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI CAFFO, BRIAN SCOTT · 2010 to 2016
$2.1M
NIBIB NIH HHS R01 EB012547NINDS NIH HHS R01 NS060910
6 · The paper itself

Abstract

We propose two fast covariance smoothing methods and associated software that scale up linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension

Indexed as

FACEfPCApenalized splinessandwich smoothersingular value decompositionsmoothing

Identifiers

PMID26903705
PMCPMC4758990

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.