A comparative study for stepwise correlated binary regression.
Real-time monitored binary data are often recorded along with a large amount of associated covariates for biomedical image processing. Serially measured binary outcomes and covariates could be autocorrelated. Appropriate variable selection schemes are necessary to find a set of influential covariates on the changes in the correlated binary outcomes. Selected variables can be used as feedback information to reduce the dimension of the database. In this context, we examine the performance of the stepwise correlated binary regression. Several realistic situations of the real-time monitored binary data are considered in Monte-Carlo simulation. Results of a simulation study are discussed.
Sohn
S Y
SY
Department of Industrial Systems Engineering, Yonsei University, Seoul, South Korea.
D000465:Algorithms; D000704:Analysis of Variance; D015982:Bias; D006801:Humans; D016015:Logistic Models; D008991:Monitoring, Physiologic; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted