Resampling-based variable selection technique and its application to model semiconductor E-test data

2010 ◽  
Author(s):  
Tsui-Chiao Chao
Keyword(s):  
Variable Selection ◽  
Test Data ◽  
Selection Technique ◽  
Model Semiconductor

scholarly journals A SIMPLE VARIABLE SELECTION TECHNIQUE FOR NONLINEAR MODELS

2001 ◽  
Vol 30 (6) ◽  
pp. 1227-1241 ◽  
Author(s):  
Gianluigi Rech ◽  
Timo Teräsvirta ◽  
Rolf Tschernig
Keyword(s):  
Variable Selection ◽  
Nonlinear Models ◽  
Selection Technique

Blind Kriging: A New Method for Developing Metamodels

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
V. Roshan Joseph ◽  
Ying Hung ◽  
Agus Sudjianto
Keyword(s):  
Experimental Data ◽  
Variable Selection ◽  
Product Design ◽  
Ordinary Kriging ◽  
Bayesian Variable Selection ◽  
Kriging Method ◽  
Selection Technique ◽  
Remarkable Improvement ◽  
Correlation Parameters ◽  
Kriging Predictor

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore, it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented, which show remarkable improvement in prediction using blind kriging over ordinary kriging. Moreover, a blind kriging predictor is easier to interpret and seems to be more robust against mis-specification in the correlation parameters.

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