Abstract
The examples in previous chapters, involving experimental settings ranging from human and animal behavior, to neuroimaging, EEG and EMG, neural spike trains, and in vitro recording, have illustrated the way statistical models describe regularity and variability of neural data.
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Notes
- 1.
From the point of view of the mathematical theory, a nonparametric method does not eliminate the parameters but rather makes them infinite dimensional.
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There is some potential for confusion because, as we said on p. 152, in the literature the “hat” sometimes denotes a generic estimator and sometimes specifies the MLE.
- 3.
More complicated formulas exist; however, the uncertainties involved in replicating results when collecting more data are often much larger than any extra precision one might gain from a more detailed calculation.
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Kass, R.E., Eden, U.T., Brown, E.N. (2014). Estimation and Uncertainty. In: Analysis of Neural Data. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9602-1_7
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DOI: https://doi.org/10.1007/978-1-4614-9602-1_7
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