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. Preprints2021, 2021020551. https://doi.org/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. https://doi.org/10.20944/preprints202102.0551.v1
Zarate-Solano, H.; Cepeda-Cuervo, E. Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family. Preprints2021, 2021020551. https://doi.org/10.20944/preprints202102.0551.v1
APA Style
Zarate-Solano, H., & Cepeda-Cuervo, E. (2021). Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family. Preprints. https://doi.org/10.20944/preprints202102.0551.v1
Chicago/Turabian Style
Zarate-Solano, H. and Edilberto Cepeda-Cuervo. 2021 "Variational Bayesian Learning and Semiparametric Models on the Double Exponential Family" Preprints. https://doi.org/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.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.