Paul R. Yarnold & Ariel Linden Optimal Data Analysis, LLC & Linden Consulting Group, LLC Optimal discriminant analysis (ODA) is often used to compare values of one (or more) attributes between two (or more) groups of observations with respect to a fixed discriminant threshold that maximizes accuracy normed against chance for the sample. However, a … Continue reading Using Fixed and Relative Optimal Discriminant Thresholds in Randomized Blocks (Matched-Pairs) Designs

Paul R. Yarnold Optimal Data Analysis, LLC Lysozyme levels in gastric juice of peptic ulcer patients were compared against normal controls by t-test, finding p<0.05. Because standard deviations differed by a factor of two between groups, and were proportional to the means, analysis of natural logarithms was instead deemed appropriate: the resulting t-test wasn’t statistically … Continue reading ODA vs. t-Test: Lysozyme Levels in the Gastric Juice of Patients with Peptic Ulcer vs. Normal Controls

Paul R. Yarnold Optimal Data Analysis, LLC Four examiners independently recorded the DMFS (decayed, missing, filled surfaces) scores of ten patients. Inter-examiner correspondence of DMFS scores was evaluated using Pearson correlation and novometric analysis. Whereas essentially perfect correlation models were unable to accurately predict DMFS scores in training analysis, novometric models were consistently perfect in … Continue reading Regression vs. Novometric-Based Assessment of Inter-Examiner Reliability

Paul R. Yarnold Optimal Data Analysis, LLC Foundational to the ODA algorithm when used with an ordered attribute is the identification of the optimal threshold—the specific cutpoint that yields the most accurate (weighted) classification solution for a sample of observations. ODA models involving a single optimal threshold will henceforth be called “fixed-threshold” models. This note … Continue reading Fixed vs. Relative Optimal Discriminant Thresholds: Pairwise Comparisons of Raters’ Ratings for a Sample

Ariel Linden, Fred B. Bryant & Paul R. Yarndol Linden Consulting Group, LLC, Loyola University Chicago & Optimal Data Analysis, LLC Recent research compared the ability of various classification algorithms [logistic regression (LR), random forests (RF), support vector machines (SVM), boosted regression (BR), multi-layer perceptron neural net model (MLP), and classification tree analysis (CTA)] to … Continue reading Logistic Discriminant Analysis and Structural Equation Modeling Both Identify Effects in Random Data