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...
প্রধান লেখক: | Salimi-Khorshidi, G, Smith, S, Nichols, T |
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বিন্যাস: | Conference item |
প্রকাশিত: |
2009
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অনুরূপ উপাদানগুলি
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Adjusting the neuroimaging statistical inferences for nonstationarity.
অনুযায়ী: Salimi-Khorshidi, G, অন্যান্য
প্রকাশিত: (2009) -
Adjusting the neuroimaging statistical inferences for nonstationarity.
অনুযায়ী: Salimi-Khorshidi, G, অন্যান্য
প্রকাশিত: (2009) -
Adjusting the effect of nonstationarity in cluster-based and TFCE inference.
অনুযায়ী: Salimi-Khorshidi, G, অন্যান্য
প্রকাশিত: (2011) -
Statistical models for neuroimaging meta-analytic inference
অনুযায়ী: Salimi-Khorshidi, G
প্রকাশিত: (2011) -
Using Gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations.
অনুযায়ী: Salimi-Khorshidi, G, অন্যান্য
প্রকাশিত: (2011)