Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Penalized splines (3)
- Functional data analysis (2)
- Accelerometer (1)
- Aggregated data (1)
- Brain imaging (1)
-
- Complex data (1)
- Contrast enhancement (1)
- DCE-MRI (1)
- Data structure (1)
- FACE (1)
- FPCA (1)
- Functional linear mixed model (1)
- Functional principal component analysis (1)
- Functional regression model (1)
- Hypothesis testing (1)
- Latent process (1)
- Mixed model (1)
- Multiple functional predictors (1)
- Multiple sclerosis (1)
- Nonparametric smoothing (1)
- Nonregular problem (1)
- Null distribution (1)
- Optimal multiple testing procedure (1)
- Pattern recognition (1)
- Physical activity (1)
- Principal components analysis (1)
- Rate of convergence (1)
- Sandwich smoother (1)
- Signal processing (1)
- Singular value decomposition (1)
Articles 1 - 8 of 8
Full-Text Articles in Biostatistics
Sparse Median Graphs Estimation In A High Dimensional Semiparametric Model, Fang Han, Han Liu, Brian Caffo
Sparse Median Graphs Estimation In A High Dimensional Semiparametric Model, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript a unified framework for conducting inference on complex aggregated data in high dimensional settings is proposed. The data are assumed to be a collection of multiple non-Gaussian realizations with underlying undirected graphical structures. Utilizing the concept of median graphs in summarizing the commonality across these graphical structures, a novel semiparametric approach to modeling such complex aggregated data is provided along with robust estimation of the median graph, which is assumed to be sparse. The estimator is proved to be consistent in graph recovery and an upper bound on the rate of convergence is given. Experiments on both …
Fast Covariance Estimation For High-Dimensional Functional Data, Luo Xiao, David Ruppert, Vadim Zipunnikov, Ciprian Crainiceanu
Fast Covariance Estimation For High-Dimensional Functional Data, Luo Xiao, David Ruppert, Vadim Zipunnikov, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
For smoothing covariance functions, we propose two fast algorithms that scale linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J x J with J>500; the recently introduced sandwich smoother is an exception, but it is not adapted to smooth covariance matrices of large dimensions such as J \ge 10,000. Covariance matrices of order J=10,000, and even J=100,000$ are becoming increasingly common, e.g., in 2- and 3-dimensional medical imaging and high-density wearable sensor data. We introduce two new algorithms that can handle very large covariance matrices: 1) FACE: a …
Soft Null Hypotheses: A Case Study Of Image Enhancement Detection In Brain Lesions, Haochang Shou, Russell T. Shinohara, Han Liu, Daniel Reich, Ciprian Crainiceanu
Soft Null Hypotheses: A Case Study Of Image Enhancement Detection In Brain Lesions, Haochang Shou, Russell T. Shinohara, Han Liu, Daniel Reich, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this …
Restricted Likelihood Ratio Tests For Functional Effects In The Functional Linear Model, Bruce J. Swihart, Jeff Goldsmith, Ciprian M. Crainiceanu
Restricted Likelihood Ratio Tests For Functional Effects In The Functional Linear Model, Bruce J. Swihart, Jeff Goldsmith, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
The goal of our article is to provide a transparent, robust, and computationally feasible statistical approach for testing in the context of scalar-on-function linear regression models. In particular, we are interested in testing for the necessity of functional effects against standard linear models. Our methods are motivated by and applied to a large longitudinal study involving diffusion tensor imaging of intracranial white matter tracts in a susceptible cohort. In the context of this study, we conduct hypothesis tests that are motivated by anatomical knowledge and which support recent findings regarding the relationship between cognitive impairment and white matter demyelination. R-code …
Optimal Tests Of Treatment Effects For The Overall Population And Two Subpopulations In Randomized Trials, Using Sparse Linear Programming, Michael Rosenblum, Han Liu, En-Hsu Yen
Optimal Tests Of Treatment Effects For The Overall Population And Two Subpopulations In Randomized Trials, Using Sparse Linear Programming, Michael Rosenblum, Han Liu, En-Hsu Yen
Johns Hopkins University, Dept. of Biostatistics Working Papers
We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear …
Structured Functional Principal Component Analysis, Haochang Shou, Vadim Zipunnikov, Ciprian Crainiceanu, Sonja Greven
Structured Functional Principal Component Analysis, Haochang Shou, Vadim Zipunnikov, Ciprian Crainiceanu, Sonja Greven
Johns Hopkins University, Dept. of Biostatistics Working Papers
Motivated by modern observational studies, we introduce a class of functional models that expands nested and crossed designs. These models account for the natural inheritance of correlation structure from sampling design in studies where the fundamental sampling 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 ultra-high dimensional data. Methods are illustrated in three examples: high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity …
Penalized Function-On-Function Regression, Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven
Penalized Function-On-Function Regression, Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven
Johns Hopkins University, Dept. of Biostatistics Working Papers
We propose a general framework for smooth regression of a functional response on one or multiple functional predictors. Using the mixed model representation of penalized regression expands the scope of function on function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed: 1) on the same or different domains than the functional response; 2) on a dense or sparse grid; and 3) with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals …
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 …