GDBE

GDBE refers to the Generalized Discriminant Bootstrap Estimator. It is a statistical method used for estimating the variance and confidence intervals of complex statistical models, particularly those involving discriminant analysis. The technique builds upon the principles of bootstrapping, a resampling method that involves repeatedly drawing samples from an observed dataset to estimate the sampling distribution of a statistic.

The "generalized" aspect of GDBE suggests its applicability to a broader range of discriminant models beyond the standard linear or quadratic discriminant analysis. This can include more sophisticated methods for classification and prediction. The "discriminant" part indicates its focus on problems where the goal is to classify observations into predefined groups based on their characteristics. The "bootstrap estimator" signifies that it employs the bootstrap method to derive its estimates of variability.

In practice, GDBE works by generating numerous bootstrap samples from the original data. For each bootstrap sample, a discriminant model is trained, and the relevant statistics are calculated. By pooling the results from all these bootstrap replications, GDBE provides estimates of the standard errors, confidence intervals, and potentially other measures of uncertainty for the parameters of the discriminant model or its predictive performance. This allows researchers and practitioners to assess the reliability of their findings from discriminant analyses.