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- Krafty, Robert T., et al. Show all 2 Author
- Journal of the American Statistical Association 2019 v.114 no.525 pp. 453-465
- Bayesian theory; El Nino; Markov chain; Monte Carlo method; electroencephalography; equations; models; sleep; time series analysis
- ... This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood ...
- Krafty, Robert T., et al. Show all 4 Authors
- Biometrics 2018 v.74 no.1 pp. 260-269
- Markov chain; Monte Carlo method; biometry; caregivers; elderly; heart rate; sleep; spectral analysis; time series analysis
- ... Many studies of biomedical time series signals aim to measure the association between frequency‐domain properties of time series and clinical and behavioral covariates. However, the time‐varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and a ...
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- Krafty, Robert T., et al. Show all 5 Authors
- Journal of the American Statistical Association 2017 v.112 no.520 pp. 1405-1416
- Markov chain; Monte Carlo method; algorithms; automation; elderly; geometry; medicine; models; sleep; spectral analysis; time series analysis
- ... This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to noninvasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified ...