Yao-San Lin

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Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination(More)
Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive, Artificial Neural Networks are widely utilized to extract management knowledge from(More)
  • Yao-San Lin
  • 2016
This article proposes a procedure for small sample regression, systematically using the concept of robust Bayesian inference and a contaminated prior. The approach explores the possible domain of population information and attempts to estimate regression parameters further. A data augmentation step included in the procedure works to enlarge the original(More)
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