Introduction to Implicit Regression

TL;DR

This paper introduces implicit regression methods, such as non-response and rotational analysis, to evaluate relationships between co-dependent variables without predefined dependent or independent variables, challenging traditional fixed effects assumptions.

Contribution

It presents novel implicit regression techniques that allow modeling of co-dependent variables without explicit response variables, expanding regression analysis capabilities.

Findings

Introduces non-response analysis and rotational analysis methods.

Challenges fixed effects assumptions in regression.

Provides measures of model quality based on degree of separation.

Abstract

Statisticians usually restrict regression to model relationships that are explicitly defined dependent and independent random variables; this paper outlines the newly developed method of non-response analysis and rotational analysis for evaluating co-dependent variables without an obvious subject response. The concepts outlined challenge the notion of fixed effects; unity is included as a random measure (variable) ignoring the assumption of independence and the degree of separation is outlined which is a measure of model quality.