Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Analysis of Complex Observational Data (4)
- Latent Variable and Measurement Error Models (4)
- Bioinformatics and Neuroinformatics (2)
- Quantitative Image Data Analysis (2)
- Auxiliary covariates; incomplete covariates; measurement error; survival; semiparametric estimation; estimated marginal partial likelihood function (1)
-
- Beta distribution (1)
- Brain (1)
- Current density reconstruction (1)
- DTI (1)
- Deconvolution (1)
- Deconvolution; unknown error; ridge-based approach; bandwidth selection; SIMEX. (1)
- Dispersion; Generalized linear mixed models; Joint modeling; Multivariate responses; Pseudo-likelihood (1)
- EEG source (1)
- EEG; Source strength; Fatigue; Muscle activation; Functional data; Functional random-effects model (1)
- Electroencephalography (EEG) (1)
- FDR under dependence (1)
- FMRI; Region of interest analysis; Number of activated voxels; Generalized negative-binomial model; Dispersion (1)
- Fourier method; (1)
- Generalized semiparametric mixed model (1)
- Handgrip force (1)
- Heteroscedasticity (1)
- INLA (1)
- MCMC (1)
- Measurement errors (1)
- Multivariate local regression; Images; Equality; Regression surfaces; Wild bootstrap; Heteroscedastic model; Spatial correlated errors (1)
- Nonparametric regression; density estimation; approximate Bayesian inference; INLA (1)
- Penalized splines (1)
- SIMEX (1)
- Simultaneous inferences (1)
- Smooth distribution function (1)
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Bayesian Inferences For Beta Semiparametric Mixed Models To Analyze Longitudinal Neuroimaging Data, Xiaofeng Wang, Yingxing Li
Bayesian Inferences For Beta Semiparametric Mixed Models To Analyze Longitudinal Neuroimaging Data, Xiaofeng Wang, Yingxing Li
Xiaofeng Wang
Diffusion tensor imaging (DTI) is a quantitative magnetic resonance imaging technique that measures the three-dimensional diffusion of water molecules within tissue through the application of multiple diffusion gradients. This technique is rapidly increasing in popularity for studying white matter properties and structural connectivity in the living human brain. The major measure derived from the DTI process is known as fractional anisotropy, a continuous measure restricted on the interval (0,1). Motivated from a DTI study of multiple sclerosis, we use a beta semiparametric mixed-effect regression model for the longitudinal neuroimaging data. This work extends the generalized additive model methodology with beta …
Bayesian Nonparametric Regression And Density Estimation Using Integrated Nested Laplace Approximations, Xiaofeng Wang
Bayesian Nonparametric Regression And Density Estimation Using Integrated Nested Laplace Approximations, Xiaofeng Wang
Xiaofeng Wang
Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach to fit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chain Monte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consuming sampling. We show here that two classical nonparametric smoothing problems, nonparametric regression and density estimation, can be achieved using INLA. Simulated examples and \texttt{R} functions are demonstrated to illustrate the use of the methods. Some discussions on potential applications of INLA are made in the paper.
Joint Generalized Models For Multi-Dimensional Outcomes: A Case Study Of Neuroscience Data From Multi-Modalities, Xiao-Feng Wang
Joint Generalized Models For Multi-Dimensional Outcomes: A Case Study Of Neuroscience Data From Multi-Modalities, Xiao-Feng Wang
Xiaofeng Wang
This paper is motivated from the analysis of neuroscience data in a study of neural and muscular mechanisms of muscle fatigue. Multidimensional outcomes of different natures were obtained simultaneously from multiple modalities, including handgrip force, electromyography (EMG), and functional magnetic resonance imaging (fMRI). We first study individual modeling of the univariate response depending on its nature. A mixed-effects beta model and a mixed-effects simplex model are compared for modeling the force/EMG percentages. A mixed-effects negative-binomial model is proposed for modeling the fMRI counts. Then, I present a joint modeling approach to model the multidimensional outcomes together, which allows us to …
The Effects Of Error Magnitude And Bandwidth Selection For Deconvolution With Unknown Error Distribution, Xiao-Feng Wang, Deping Ye
The Effects Of Error Magnitude And Bandwidth Selection For Deconvolution With Unknown Error Distribution, Xiao-Feng Wang, Deping Ye
Xiaofeng Wang
The error distribution is generally unknown in deconvolution problems with real applications. A separate independent experiment is thus often conducted to collect the additional noise data in those studies. In this paper, we study the nonparametric deconvolution estimation from a contaminated sample coupled with an additional noise sample. A ridge-based kernel deconvolution estimator is proposed and its asymptotic properties are investigated depending on the error magnitude. We then present a data-driven bandwidth selection algorithm with combining the bootstrap method and the idea of simulation extrapolation. The finite sample performance of the proposed methods and the effects of error magnitude are …
A Generalized Regression Model For Region Of Interest Analysis Of Fmri Data, Xiao-Feng Wang, Zhiguo Jiang, Janis J. Daly, Guang H. Yue
A Generalized Regression Model For Region Of Interest Analysis Of Fmri Data, Xiao-Feng Wang, Zhiguo Jiang, Janis J. Daly, Guang H. Yue
Xiaofeng Wang
In this study functional Magnetic Resonance Imaging (fMRI) was used to evaluate cortical motor network adaptation after a rehabilitation program for upper extremity motor function in chronic stroke patients. Patients and healthy controls were imaged when they attempted to perform shoulder–elbow and wrist–hand movements in a 1.5 T Siemens scanner. We perform fMRI analysis at both single- and group-subject levels. Activated voxel counts are calculated to quantify brain activation in regions of interest. We discuss several candidate regression models for making inference on the count data, and propose an application of a generalized negative-binomial model (GNBM) with structured dispersion in …
Time-Dependent Cortical Activation In Voluntary Muscle Contraction, Qi Yang, Xiao-Feng Wang, Yin Fang, Vlodek Siemionow, Wanxiang Yao, Guang H. Yue
Time-Dependent Cortical Activation In Voluntary Muscle Contraction, Qi Yang, Xiao-Feng Wang, Yin Fang, Vlodek Siemionow, Wanxiang Yao, Guang H. Yue
Xiaofeng Wang
This study was to characterize dynamic source strength changes estimated from high-density scalp electroencephalogram (EEG) at different phases of a submaximal voluntary muscle contraction. Eight healthy volunteers performed isometric handgrip contractions of the right arm at 20% maximal intensity. Signals of the handgrip force, electromyography (EMG) from the finger flexor and extensor muscles and 64-channel EEG were acquired simultaneously. Sources of the EEG were analyzed at 19 time points across preparation, execution and sustaining phases of the handgrip. A 3-layer boundary element model (BEM) based on the MNI (Montréal Neurological Institute) brain MRI was used to overlay the sources. A …
On Nonparametric Comparison Of Images And Regression Surfaces, Xiao-Feng Wang, Deping Ye
On Nonparametric Comparison Of Images And Regression Surfaces, Xiao-Feng Wang, Deping Ye
Xiaofeng Wang
Multivariate local regression is an important tool for image processing and analysis. In many practical biomedical problems, one is often interested in comparing a group of images or regression surfaces. In this paper, we extend the existing method of testing the equality of nonparametric curves by Dette and Neumeyer (2001) and consider a test statistic by means of an L2-distance in the multi-dimensional case under a completely heteroscedastic nonparametric model. The test statistic is also extended to be used in the case of spatial correlated errors. Two bootstrap procedures are described in order to approximate the critical values of the …
Estimating Smooth Distribution Function In The Presence Of Heteroscedastic Measurement Errors, Xiao-Feng Wang, Zhaozhi Fan, Bin Wang
Estimating Smooth Distribution Function In The Presence Of Heteroscedastic Measurement Errors, Xiao-Feng Wang, Zhaozhi Fan, Bin Wang
Xiaofeng Wang
Measurement error occurs in many biomedical fields. The challenges arise when errors are heteroscedastic since we literally have only one observation for each error distribution. This paper concerns the estimation of smooth distribution function when data are contaminated with heteroscedastic errors. We study two types of methods to recover the unknown distribution function: a Fourier-type deconvolution method and a simulation extrapolation (SIMEX) method. The asymptotics of the two estimators are explored and the asymptotic pointwise confidence bands of the SIMEX estimator are obtained. The finite sample performances of the two estimators are evaluated through a simulation study. Finally, we illustrate …
Marginal Hazards Model For Multivariate Failure Time Data With Auxiliary Covariates, Zhaozhi Fan, Xiao-Feng Wang
Marginal Hazards Model For Multivariate Failure Time Data With Auxiliary Covariates, Zhaozhi Fan, Xiao-Feng Wang
Xiaofeng Wang
A marginal hazards model of multivariate failure times has been developed based on the ‘working independence’ assumption [L.J. Wei, D.Y. Lin, and L. Wessfeld, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, J. Amer. Statist. Assoc. 84 (1989), pp. 1065–1073.]. In this article, we study the marginal hazards model of multivariate failure times with continuous auxiliary covariates. We consider the case of common baseline hazards for subjects from the same clusters. We extend the kernel smoothing procedure of Zhou and Wang [H. Zhou and C.Y. Wang, Failure time regression with continuous covariates measured with error, J. …
Assessing Time-Dependent Association Between Scalp Eeg And Muscle Activation: A Functional Random-Effects Model Approach, Xiao-Feng Wang, Qi Yang, Zhaozhi Fan, Chang-Kai Sun, Guang H. Yue
Assessing Time-Dependent Association Between Scalp Eeg And Muscle Activation: A Functional Random-Effects Model Approach, Xiao-Feng Wang, Qi Yang, Zhaozhi Fan, Chang-Kai Sun, Guang H. Yue
Xiaofeng Wang
This study investigates time-dependent associations between source strength estimated from high-density scalp electroencephalogram (EEG) and force of voluntary handgrip contraction at different intensity levels. We first estimate source strength from raw EEG signals collected during voluntary muscle contractions at different levels and then propose a functional random-effects model approach in which both functional fixed effects and functional random-effects are considered for the data. Two estimation procedures for the functional model are discussed. The first estimation procedure is a two-step method which involves no iterations. It can flexibly use different smoothing methods and smoothing parameters. The second estimation procedure benefits from …
Modeling Heterogeneity And Dependence For Analysis Of Neuronal Data, Xiao-Feng Wang, Jiayang Sun, Kenneth J. Gustafson, Guang H. Yue
Modeling Heterogeneity And Dependence For Analysis Of Neuronal Data, Xiao-Feng Wang, Jiayang Sun, Kenneth J. Gustafson, Guang H. Yue
Xiaofeng Wang
In this paper, we describe two types of neuroscience problems which challenge the typical statistical models assumed for analyzing neuronal data. This offers an opportunity for new modeling and statistical inference. In the first problem, the data are spatial neural counts which are often over-dispersed and spatially correlated so that a standard Poisson regression model is inadequate. In the second problem, the data are averaged electroencephalograph signals recorded during muscle fatigue, where a time series AR(1) regression model cannot fully capture all the variation and correlation structure in the data. It is shown that an additional parameter has to be …
Spatial-Temporal Data Mining Procedure: Lasr, Xiao-Feng Wang, Jiayang Sun, Kath Bogie
Spatial-Temporal Data Mining Procedure: Lasr, Xiao-Feng Wang, Jiayang Sun, Kath Bogie
Xiaofeng Wang
This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced "laser"). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of largep-small-n data. It was motivated by a study of "Neuromuscular Electrical Stimulation" experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of multiple comparisons. The three main components of LASR are: (1) data segmentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying "activated" regions based …