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Model selection in electromagnetic source analysis with an application to VEF’s
 IEEE Transactions on Biomedical Engineering
, 2002
"... Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated i ..."
Abstract

Cited by 7 (4 self)
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Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.
Goodnessoffit and confidence intervals of approximate models
"... To test whether the model fits the data well, a goodnessoffit (GOF) test can be used. The chisquare GOF test is often used to test the null hypothesis that a function describes the mean of the data well. The null hypothesis with this test is rejected too often, however, because the nominal signif ..."
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Cited by 1 (1 self)
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To test whether the model fits the data well, a goodnessoffit (GOF) test can be used. The chisquare GOF test is often used to test the null hypothesis that a function describes the mean of the data well. The null hypothesis with this test is rejected too often, however, because the nominal significance level (usually 0.05) is exceeded. Alternatively, the level of Hotelling’s test is accurate if a fixed hypothesis for the mean is available. In many situations, however, only an estimate of the mean is available, and so the level of Hotelling’s test may also be incorrect. An approximate version of Hotelling’s test is suggested as a GOF test. It is shown that this requires only an adjustment of the degrees of freedom of Hotelling’s original test. GOF tests assume that the model is either correct or incorrect whereas in model specification it is often assumed that the model is an approximation. Consequently, for approximate models a GOF test will mostly indicate that the model does not fit. It is therefore suggested that a measure of approximation to the true model could be used to get an indication of how bad the approximate model is. It is also shown that correct confidence intervals can be obtained from when using an approximate model. The results are applied to data from the daily news memory test. 1