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- Keyword
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- Analysis of Complex Observational Data (1)
- Deconvolution; unknown error; ridge-based approach; bandwidth selection; SIMEX. (1)
- Dispersion; Generalized linear mixed models; Joint modeling; Multivariate responses; Pseudo-likelihood (1)
- FMRI; Region of interest analysis; Number of activated voxels; Generalized negative-binomial model; Dispersion (1)
- Latent Variable and Measurement Error Models (1)
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
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 …