Combining Information From Various Sources: A Prediction Problem and Other Industrial Applications
作者:
G.J. Hahn,
T.E. Raghunathan,
期刊:
Technometrics
(Taylor Available online 1988)
卷期:
Volume 30,
issue 1
页码: 41-52
ISSN:0040-1706
年代: 1988
DOI:10.1080/00401706.1988.10488321
出版商: Taylor & Francis Group
关键词: Bayesian methods;Kalman filter;Pooling data;Prediction intervals;Random-effects models
数据来源: Taylor
摘要:
Industrial problems frequently require estimates from various sources of information. For example, one may need to predict the tensile strength of a future bar from a particular casting based on limited data on other bars from that casting and extensive data on bars from other castings. Or one may wish to estimate the true viscosity of a batch of material based on a single measurement for the current batch, subject to appreciable measurement error, and similar readings on a large number of other batches. Simple weighting functions that use all of the data provide point estimates for these two problems, and a Bayesian framework yields associated interval estimates. Other applications and possible generalizations are also suggested.
点击下载:
PDF (1106KB)
返 回