Review of ODA 1.0 optimal data analysis for DOSTM

Statistics and Computing -     

Theoretical Distributions of Optima for Univariate Discrimination of Random Data*

Optimal linear discriminant models maximize percentage accuracy for dichotomous classifications, but are rarely used because a theoretical framework that allows one to make valid statements about the statistical significance of the outcomes of such analyses does not exist. This paper describes an analytic solution for the theoretical distribution of optimal values for univariate optimal linear discriminant analysis, under the assumption that the data are random and continuous. We also present the theoretical distribution for sample sizes up to N= 30. The discovery of a statistical framework for evaluating the performance of optimal discriminant models should greatly increase their use by scientists in all disciplines.     

Refining Two-Group Multivariable Classification Models Using Univariate Optimal Discriminant Analysis*

Fisher's discriminant analysis (FDA) is often used to obtain a prediction model for dichotomous classifications on the basis of two or more independent variables. FDA provides an equation whereby values on independent variables are combined into a single predicted value (Y*) that is compared against a cutpoint and direction in order to make classifications. Theoretically, univariate optimal discriminant analysis employed on these Y* will maximize training classification accuracy. This methodology is illustrated using three examples.     

Embedding Expertise for Policy Responses to COVID-19: Comparing Decision-Making Structures in Two Federal Democracies

Public Organization Review - The COVID-19 crisis focused attention on how experts from different scientific fields provided advice to governments through expert committees and task forces. We...