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We consider the problem of testing null hypotheses that include restrictions on the variance component in a linear mixed model with one variance component. We derive the finite sample and asymptotic distribution of the likelihood ratio test (LRT) and the restricted likelihood ratio test (RLRT). The spectral representations of the LRT and RLRT statistics are(More)
Subdural electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery have shown that functional activation is associated with event-related broadband gamma activity in a higher frequency range (>70 Hz) than previously studied in human scalp EEG. To investigate the utility of this high gamma activity (HGA) for mapping language cortex, we(More)
The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address(More)
We develop fast fitting methods for generalized functional linear models. The functional predictor is projected onto a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression; confidence intervals based on the mixed model framework are obtained. Our method can be applied to many functional data designs(More)
BACKGROUND Short-term variability in measures of glycemia has important implications for the diagnosis of diabetes mellitus and the conduct and interpretation of epidemiologic studies. Our objectives were to characterize the within-person variability in fasting glucose, 2-hour glucose, and hemoglobin A1c (HbA1c) levels and to assess the impact of using(More)
OBJECTIVE To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN Cross-sectional study. SETTING Johns Hopkins University, Baltimore, Maryland. (More)
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact(More)
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The(More)
BACKGROUND AND PURPOSE Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls(More)
Capture-recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture-recapture models. A fully Bayesian approach using(More)