TY - GEN
T1 - A model validation method with bootstrap approach and Bayes estimation for small sample
AU - Song, Ting
AU - Ma, Ping
AU - Zhou, Yuchen
AU - Fang, Ke
AU - Yang, Ming
N1 - Publisher Copyright:
© Institute of Information Science. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Generally, model validation is mainly based on statistical analysis. However, when the sample size of real system output is small, it is difficult to obtain accurate validation results with classical statistics theory. In such a situation, a model validation method based on improved Bootstrap approach and Bayes estimation is provided. First, Bootstrap method is used to enlarge observed samples size and obtain Bayes prior distribution information. Then, Bayes theory which combines prior information and small sample data is used to estimate the statistical characteristics of observed samples. Finally, single-sample hypothesis testing is used to evaluate the credibility of simulation model. Furthermore, an improved Bootstrap method is proposed, which raises the accuracy of parameter estimation and extends bootstrap samples range beyond the original data. The numerical experiment results reveal the effectiveness of validation method and improved Bootstrap method.
AB - Generally, model validation is mainly based on statistical analysis. However, when the sample size of real system output is small, it is difficult to obtain accurate validation results with classical statistics theory. In such a situation, a model validation method based on improved Bootstrap approach and Bayes estimation is provided. First, Bootstrap method is used to enlarge observed samples size and obtain Bayes prior distribution information. Then, Bayes theory which combines prior information and small sample data is used to estimate the statistical characteristics of observed samples. Finally, single-sample hypothesis testing is used to evaluate the credibility of simulation model. Furthermore, an improved Bootstrap method is proposed, which raises the accuracy of parameter estimation and extends bootstrap samples range beyond the original data. The numerical experiment results reveal the effectiveness of validation method and improved Bootstrap method.
KW - Bayes estimation
KW - Improved Bootstrap method
KW - Model validation
KW - Small sample
UR - https://www.scopus.com/pages/publications/85056744691
M3 - 会议稿件
AN - SCOPUS:85056744691
T3 - 30th European Modeling and Simulation Symposium, EMSS 2018
SP - 74
EP - 80
BT - 30th European Modeling and Simulation Symposium, EMSS 2018
A2 - Merkuryev, Yuri
A2 - Piera, Miquel Angel
A2 - Longo, Francesco
A2 - Bruzzone, Agostino G.
A2 - Affenzeller, Michael
A2 - Jimenez, Emilio
PB - Dime University of Genoa
T2 - 30th European Modeling and Simulation Symposium, EMSS 2018
Y2 - 17 September 2018 through 19 September 2018
ER -