TY - JOUR
T1 - Bayesian Nonparametric Regression Modeling of Panel Data for Sequential Classification
AU - Xiong, Sihan
AU - Fu, Yiwei
AU - Ray, Asok
N1 - Funding Information:
Manuscript received January 9, 2017; revised June 5, 2017, August 3, 2017, and September 1, 2017; accepted September 8, 2017. Date of publication October 12, 2017; date of current version August 20, 2018. This work was supported by the U.S. Air Force Office of Scientific Research under Grant FA9550-15-1-0400. (Corresponding author: Asok Ray.) The authors are with the Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802-1412 USA (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2012 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates model interpretation by identifying statistically significant predictors with hypothesis testing. Moreover, as the data length approaches infinity, posterior consistency of the model is guaranteed for general data-generating processes under regular conditions. The resulting model of panel data can also be used for sequential classification. The proposed method has been tested by numerical simulation, then validated on an econometric public data set, and subsequently validated for detection of combustion instabilities with experimental data that have been generated in a laboratory environment.
AB - This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates model interpretation by identifying statistically significant predictors with hypothesis testing. Moreover, as the data length approaches infinity, posterior consistency of the model is guaranteed for general data-generating processes under regular conditions. The resulting model of panel data can also be used for sequential classification. The proposed method has been tested by numerical simulation, then validated on an econometric public data set, and subsequently validated for detection of combustion instabilities with experimental data that have been generated in a laboratory environment.
UR - http://www.scopus.com/inward/record.url?scp=85052685288&partnerID=8YFLogxK
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U2 - 10.1109/TNNLS.2017.2752005
DO - 10.1109/TNNLS.2017.2752005
M3 - Article
C2 - 29035227
AN - SCOPUS:85052685288
SN - 2162-237X
VL - 29
SP - 4128
EP - 4139
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 9
M1 - 8066450
ER -