F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

Abstract

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ 2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ 2(F)/F 2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

Stability indicating HPTLC Method Development and Validation for Estimation of Glucosamine and Diacerein as Bulk Drug and in Drug Formulation by Derivatization

“…Further method sensitivity was determined form the calculated values of LOD and LOQ by using equation as described in procedure section.The LOD and LOQ values were found to be 33.02, 100.07 ng/spot for glucosamine and 4.65, 14.09 ng/spot for diacerein, respectively. To further confirm linearity, F test was applied where the value of experimental Fischer ratio was compared against the critical value found in statistical tables for both analytes; experimental F ratio values were found to be less than the tabulated value at the 95% confidence level 34 . Furthermore the residual plots of relative response against concentration were plotted for glucosamine and diacerein as presented in Figure 3, where residual plots show no trending.…”