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Full-Text Articles in Physical Sciences and Mathematics
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow …
Predicting Human Movement Type Based On Multiple Accelerometers Using Movelets, Bing He, Jiawei Bai, Annemarie Koster, Casserotti Paolo, Nancy Glynn, Tamara B. Harris, Ciprian Crainiceanu
Predicting Human Movement Type Based On Multiple Accelerometers Using Movelets, Bing He, Jiawei Bai, Annemarie Koster, Casserotti Paolo, Nancy Glynn, Tamara B. Harris, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We introduce statistical methods for prediction of types of human movement based on three tri-axial accelerometers worn simultaneously at the hip, left, and right wrist. We compare the individual performance of the three accelerometers using movelets and propose a new prediction algorithm that integrates the information from all three accelerometers. The development is motivated by a study of 20 older subjects who were instructed to perform 15 different types of activities during in-laboratory sessions. The differences in the prediction performance for different activity types among the three accelerometers reveal subtle yet important insights into how the intrinsic physical features of …
Model Choice In Time Series Studies Of Air Pollution And Mortality, Roger D. Peng, Francesca Dominici, Thomas A. Louis
Model Choice In Time Series Studies Of Air Pollution And Mortality, Roger D. Peng, Francesca Dominici, Thomas A. Louis
Johns Hopkins University, Dept. of Biostatistics Working Papers
Multi-city time series studies of particulate matter (PM) and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the United States National Ambient Air Quality Standards (NAAQS) for PM. As a result, methodological issues concerning time series analysis of the relation between air pollution and health have attracted the attention of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analyzed …