Biometrics, 2015-03, Vol.71 (1), p.247-257
2015
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Details
- Autor(en) / Beteiligte
- Titel
- Structured functional principal component analysis
- Ist Teil von
- Biometrics, 2015-03, Vol.71 (1), p.247-257
- Ort / Verlag
- United States: International Biometric Society, etc.
- Erscheinungsjahr
- 2015
- Quelle
- Wiley-Blackwell Journals
- Beschreibungen/Notizen
- Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high‐dimensional data. Methods are used in applications including high‐frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.
- Sprache
- Englisch; Französisch
- Identifikatoren
- ISSN: 0006-341XeISSN: 1541-0420DOI: 10.1111/biom.12236Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4383722
- Format
- –
- Schlagworte
- Algorithms, BIOMETRIC PRACTICE, biometry, brain, covariance, Data Interpretation, Statistical, Diagnosis, Computer-Assisted - methods, Electroencephalography, Functional linear mixed model, Functional principal component analysis, Humans, Latent process, Monitoring, Physiologic - methods, Multilevel correlation structure, Numerical Analysis, Computer-Assisted, observational studies, Principal Component Analysis, Principal components analysis, Reproducibility of Results, Sensitivity and Specificity, Sleep, Variance component