TY - JOUR
T1 - A data augmentation approach for a class of statistical inference problems
AU - Carvajal, Rodrigo
AU - Orellana, Rafael
AU - Katselis, Dimitrios
AU - Escárate, Pedro
AU - Agüero, Juan Carlos
N1 - Funding Information:
This work was partially supported by the Fondo Nacional de Desarrollo Científico y Tecnológico-Chile through grants No. 3140054 and 1181158. This work was also partially supported by the Comisión Nacional de Investigación Científica y Tecnológica, Advanced Center for Electrical and Electronic Engineering (AC3E, Proyecto Basal FB0008), Chile. The work of R. Orellana was partially supported by the PIIC program of the Universidad Técnica Federico Santa María, scholarship 015/2018. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was partially supported by the Fondo Nacional de Desarrollo Científico y Tecnoló-gico-Chile through grants No. 3140054 and 1181158. This work was also partially supported by the Comisión Nacional de Investigación Científica y Tecnológica, Advanced Center for Electrical and Electronic Engineering (AC3E, Proyecto Basal FB0008), Chile. The work of R. Orellana was partially supported by the PIIC program of the Universidad Técnica Federico Santa María, scholarship 015/2018. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2018 Carvajal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/12
Y1 - 2018/12
N2 - We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization algorithm. The resulting algorithm can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori estimation problems, Instrumental Variables, Regularized Optimization and Constrained Optimization problems. The advantage of the proposed algorithm is to provide a systematic procedure to build surrogate functions for a class of problems where hidden variables are usually involved. Numerical examples show the benefits of the proposed approach.
AB - We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization algorithm. The resulting algorithm can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori estimation problems, Instrumental Variables, Regularized Optimization and Constrained Optimization problems. The advantage of the proposed algorithm is to provide a systematic procedure to build surrogate functions for a class of problems where hidden variables are usually involved. Numerical examples show the benefits of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85058233969&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0208499
DO - 10.1371/journal.pone.0208499
M3 - Article
C2 - 30532211
AN - SCOPUS:85058233969
VL - 13
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 12
M1 - e0208499
ER -