TY - JOUR
T1 - Asymptotic Theory of Taguchis Natural Estimators of the Signal to Noise Ratio for Dynamic Robust Parameter Design
AU - Tsukuda, Koji
AU - Nagata, Yasushi
N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number 24500351.
Publisher Copyright:
© Taylor & Francis Group, LLC.
PY - 2015/11/17
Y1 - 2015/11/17
N2 - This article discusses the asymptotic theory of Taguchis natural estimators of the signal to noise ratio (SNR) for dynamic robust parameter design. Three asymptotic properties are shown. First, two natural estimators of the population SNR are asymptotically equivalent. Second, both of these estimators are consistent. Finally, both of these estimators are asymptotically normally distributed.
AB - This article discusses the asymptotic theory of Taguchis natural estimators of the signal to noise ratio (SNR) for dynamic robust parameter design. Three asymptotic properties are shown. First, two natural estimators of the population SNR are asymptotically equivalent. Second, both of these estimators are consistent. Finally, both of these estimators are asymptotically normally distributed.
KW - Asymptotic normality
KW - Consistency
KW - Robust parameter design
KW - Signal to noise ratio
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U2 - 10.1080/03610926.2013.809120
DO - 10.1080/03610926.2013.809120
M3 - Article
AN - SCOPUS:84947547292
SN - 0361-0926
VL - 44
SP - 4734
EP - 4741
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 22
ER -