Generalized likelihood ratio tests for complex fMRI data: a Simulation study

Generalized likelihood ratio tests

Generalized Likelihood Ratio Tests for Additive Models

Composite Hypotheses and Generalized Likelihood Ratio Tests

In many real world problems, it is difficult to precisely specify probability distributions. Our models for data may involve unknown parameters or other characteristics. Here are a few motivating examples. Example: Unknown amplitudes/delays in wireless communications. We don't always know how many relays a signal will go through, how strong the signal will be at each receiver, the distance betw...

Implications of the Rician distribution for fMRI generalized likelihood ratio tests.

In functional magnetic resonance imaging (fMRI), the general linear model test (GLMT) is widely used for brain activation detection. However, the GLMT relies on the assumption that the noise corrupting the data is Gaussian distributed. Because the majority of fMRI studies employ magnitude image reconstructions, which are Rician distributed, this assumption is invalid and has significant consequ...

Likelihood Ratio Tests for