A Generative Approach to Joint Modeling of Quantitative and Qualitative Responses

TL;DR

This paper introduces a generative modeling approach for jointly analyzing quantitative and qualitative responses alongside predictors, improving classification and prediction accuracy with efficient computation.

Contribution

It proposes a novel generative framework that models the joint distribution of responses and predictors, leveraging predictor dependencies for better performance.

Findings

Achieves accurate classification and prediction in simulations and real data.

Provides asymptotic optimality under regularity conditions.

Demonstrates effectiveness in material science and genetics case studies.

Abstract

In many scientific areas, data with quantitative and qualitative (QQ) responses are commonly encountered with a large number of predictors. By exploring the association between QQ responses, existing approaches often consider a joint model of QQ responses given the predictor variables. However, the dependency among predictive variables also provides useful information for modeling QQ responses. In this work, we propose a generative approach to model the joint distribution of the QQ responses and predictors. The proposed generative model provides efficient parameter estimation under a penalized likelihood framework. It achieves accurate classification for qualitative response and accurate prediction for quantitative response with efficient computation. Because of the generative approach framework, the asymptotic optimality of classification and prediction of the proposed method can be established under some regularity conditions. The performance of the proposed method is examined through simulations and real case studies in material science and genetics.