Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters

2010 ◽  
Vol 32 (8) ◽  
pp. 1522-1528 ◽  
Author(s):  
Tobias Glasmachers ◽  
Christian Igel
Keyword(s):  
Maximum Likelihood ◽  
Model Selection ◽  
Multiple Parameters ◽  
Soft Margin ◽  
Likelihood Model ◽  
Selection For

Model Selection for Convolutive ICA with an Application to Spatiotemporal Analysis of EEG

2007 ◽  
Vol 19 (4) ◽  
pp. 934-955 ◽  
Author(s):  
Mads Dyrholm ◽  
Scott Makeig ◽  
Lars Kai Hansen
Keyword(s):  
Maximum Likelihood ◽  
Model Selection ◽  
Bayesian Model ◽  
Spatiotemporal Analysis ◽  
Realistic Model ◽  
Mixing Process ◽  
Eeg Signals ◽  
Independent Components ◽  
Selection For ◽  
Initial Results

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.

scholarly journals Estimating Population Mean Power Under Conditions of Heterogeneity and Selection for Significance

2020 ◽  
Vol 4 ◽  
Author(s):  
Jerry Brunner ◽  
Ulrich Schimmack
Keyword(s):  
Maximum Likelihood ◽  
Statistical Power ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Significance Tests ◽  
Mean Power ◽  
Population Mean ◽  
Likelihood Model ◽  
Selection For ◽  
Z Curve

In scientific fields that use significance tests, statistical power is important for successful replications of significant results because it is the long-run success rate in a series of exact replication studies. For any population of significant results, there is a population of power values of the statistical tests on which conclusions are based. We give exact theoretical results showing how selection for significance affects the distribution of statistical power in a heterogeneous population of significance tests. In a set of large-scale simulation studies, we compare four methods for estimating population mean power of a set of studies selected for significance (a maximum likelihood model, extensions of p-curve and p-uniform, & z-curve). The p-uniform and p-curve methods performed well with a fixed effects size and varying sample sizes. However, when there was substantial variability in effect sizes as well as sample sizes, both methods systematically overestimate mean power. With heterogeneity in effect sizes, the maximum likelihood model produced the most accurate estimates when the distribution of effect sizes matched the assumptions of the model, but z-curve produced more accurate estimates when the assumptions of the maximum likelihood model were not met. We recommend the use of z-curve to estimate the typical power of significant results, which has implications for the replicability of significant results in psychology journals.

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