传统的功能连接网络模型只提取功能磁共振成像(functional Magnetic Resonance Imaging,fMRI)感兴趣区域(Regions Of Interest,ROIs)的时域特征,用于阿尔茨海默症(Alzheimer's Disease,AD)分类。该模型忽略了ROIs的空域特征,例如脑...传统的功能连接网络模型只提取功能磁共振成像(functional Magnetic Resonance Imaging,fMRI)感兴趣区域(Regions Of Interest,ROIs)的时域特征,用于阿尔茨海默症(Alzheimer's Disease,AD)分类。该模型忽略了ROIs的空域特征,例如脑区之间的两两相关性。不完整的时空特征直接影响阿尔茨海默症、晚期认知障碍(late Mild Cognitive Impairment,lMCI)、早期认知障碍(early Mild Cognitive Impairment,eMCI)和健康对照(Healthy Controls,HC)分类任务的精确度。提出使用动态相关系数核(称为DC-kernel)对空域特征进行动态获取,并与时域特征进行特征融合。实验结果表明,在AD、lMCI、eMCI和HC四分类任务中,较传统阿尔茨海默症的准确率有提高,为其它脑疾病的分类任务提供了一种新的思路。展开更多
We study tile local linear estimator for tile drift coefficient of stochastic differential equations driven by α-stable Levy motions observed at discrete instants. Under regular conditions, we derive the weak consis-...We study tile local linear estimator for tile drift coefficient of stochastic differential equations driven by α-stable Levy motions observed at discrete instants. Under regular conditions, we derive the weak consis- tency and central limit theorem of the estimator. Compared with Nadaraya-Watson estimator, the local linear estimator has a bias reduction whether the kernel function is symmetric or not under different schemes. A silnu- lation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator, especially on the boundary.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11171303 and 11071213)the Specialized Research Fund for the Doctor Program of Higher Education(Grant No.20090101110020)
文摘We study tile local linear estimator for tile drift coefficient of stochastic differential equations driven by α-stable Levy motions observed at discrete instants. Under regular conditions, we derive the weak consis- tency and central limit theorem of the estimator. Compared with Nadaraya-Watson estimator, the local linear estimator has a bias reduction whether the kernel function is symmetric or not under different schemes. A silnu- lation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator, especially on the boundary.
