Adjusting the neuroimaging statistical inferences for nonstationarity.
In neuroimaging cluster-based inference has generally been found to be more powerful than voxel-wise inference. However standard cluster-based methods assume stationarity (constant smoothness), while under nonstationarity clusters are larger in smooth regions just by chance, making false positive ri...
Những tác giả chính: | Salimi-Khorshidi, G, Smith, S, Nichols, T |
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Định dạng: | Conference item |
Được phát hành: |
2009
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Những quyển sách tương tự
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Adjusting the neuroimaging statistical inferences for nonstationarity.
Bằng: Salimi-Khorshidi, G, et al.
Được phát hành: (2009) -
Adjusting the neuroimaging statistical inferences for nonstationarity.
Bằng: Salimi-Khorshidi, G, et al.
Được phát hành: (2009) -
Adjusting the effect of nonstationarity in cluster-based and TFCE inference.
Bằng: Salimi-Khorshidi, G, et al.
Được phát hành: (2011) -
Statistical models for neuroimaging meta-analytic inference
Bằng: Salimi-Khorshidi, G
Được phát hành: (2011) -
Using Gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations.
Bằng: Salimi-Khorshidi, G, et al.
Được phát hành: (2011)