Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family

Version 1 : Received: 23 February 2021 / Approved: 24 February 2021 / Online: 24 February 2021 (15:18:37 CET)

How to cite: Zarate-Solano, H.; Cepeda-Cuervo, E. Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family. Preprints 2021, 2021020551 (doi: 10.20944/preprints202102.0551.v1). Zarate-Solano, H.; Cepeda-Cuervo, E. Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family. Preprints 2021, 2021020551 (doi: 10.20944/preprints202102.0551.v1).

Abstract

In this paper, we focus on variational Bayesian learning deterministic optimization methods for inference in biparametric exponential models where the parameters follow semiparametric regression structures. This combination of data models and algorithms contributes to solving real-world problems and reduces the computation time. This allows both the rapid exploration of many data models and the accurate estimation of the mean and variance functions through the connection between generalized linear models and graph theory. A simulation study was carried out to assess the performance of the deterministic approximation. Finally, herein, we present an application using macroeconomic data to emphasize the benefits of the proposed approach.

Subject Areas

Variational learning Bayes; semiparametric heterocedastic models; calculus of variations; optimization; biparametric exponential models

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