
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
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A relation between loglikelihood and crossvalidation logscores
It is shown that the loglikelihood of a hypothesis or model given some ...
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Classical Statistics and Statistical Learning in Imaging Neuroscience
Neuroimaging research has predominantly drawn conclusions based on class...
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Approximate leavefutureout crossvalidation for time series models
One of the common goals of time series analysis is to use the observed s...
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Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) d...
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Computing AIC for blackbox models using Generalised Degrees of Freedom: a comparison with crossvalidation
Generalised Degrees of Freedom (GDF), as defined by Ye (1998 JASA 93:120...
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Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data
Mass cytometry technology enables the simultaneous measurement of over 4...
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MassUnivariate Hypothesis Testing on MEEG Data using CrossValidation
Recent advances in statistical theory, together with advances in the computational power of computers, provide alternative methods to do massunivariate hypothesis testing in which a large number of univariate tests, can be properly used to compare MEEG data at a large number of timefrequency points and scalp locations. One of the major problematic aspects of this kind of massunivariate analysis is due to high number of accomplished hypothesis tests. Hence procedures that remove or alleviate the increased probability of false discoveries are crucial for this type of analysis. Here, I propose a new method for massunivariate analysis of MEEG data based on crossvalidation scheme. In this method, I suggest a hierarchical classification procedure under kfold crossvalidation to detect which sensors at which timebin and which frequencybin contributes in discriminating between two different stimuli or tasks. To achieve this goal, a new feature extraction method based on the discrete cosine transform (DCT) employed to get maximum advantage of all three data dimensions. Employing crossvalidation and hierarchy architecture alongside the DCT feature space makes this method more reliable and at the same time enough sensitive to detect the narrow effects in brain activities.
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