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ESL-Based Robust Estimation for Mean-Covariance Regression with Longitudinal Data

Open Journal of Statistics 2020年第1期10卷 10-30页

作者： Fei Lu Liugen Xue Xiong Cai College of Applied Sciences Beijing University of Technology Beijing China

When longitudinal data contains outliers, the classical least-squares approach is known to be not robust. To solve this issue, the exponential squared loss (ESL) function with a tuning parameter has been investigated for longitudinal data. However, to our knowledge, there is no paper to investigate the robust estimation procedure against outliers within the framework of mean-covariance regression analysis for longitudinal data using the ESL function. In this paper, we propose a robust estimation approach for the model parameters of the mean and generalized autoregressive parameters with longitudinal data based on the ESL function. The proposed estimators can be shown to be asymptotically normal under certain conditions. Moreover, we develop an iteratively reweighted least squares (IRLS) algorithm to calculate the parameter estimates, and the balance between the robustness and efficiency can be achieved by choosing appropriate data adaptive tuning parameters. Simulation studies and real data analysis are carried out to illustrate the finite sample performance of the proposed approach.

关键词： Exponential Squared Loss Function within-subject Correlation Longitudinal Data Modified Cholesky Decomposition Robustness

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Stability of fMRI activation:evidence from fusiform face area

Stability of fMRI activation:evidence from fusiform face are...

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中国神经科学学会第九届全国学术会议暨第五次会员代表大会

作者： Bing CHEN~(1,2),Wei LEI~3,Jie XI~4,Xu-Chu WENG~(2,*) 1 Xiamen University,Xiamen 361005,China 2 Hangzhou Normal University,Hangzhou 310015,China 3 Southwest University,Chongqing 400715,China 4 Institute of Psychology,Chinese Academy of Sciences,Beijing 10010,China

＜正＞Objective Fusiform face area（FFA） is highly specialized for faces and independent of task modulation. This study was to test the stability of FFA response to face information across 4 fMRI scans spanning one year in the same group of *** Block design was adopted and the stimuli included English words,Chinese characters,and *** Linear Model（GLM） was performed to define the ROI（region of interested）,i.e.,the FFA,in each *** stability of space coordinates of activation voxels within the ROI was analyzed by within-subject *** The coordinates of the voxels in the FFA ROI were highly reproducible across the 4 scans whether the ROI was defined by face *** or face *** The position of the FFA measured by fMRI is remarkably *** is feasible to use the FFA as a reference region in fMRI studies on the visual system.

关键词： Fusiform face area General Linear Model within-subject

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