Multivariate Process Analysis With Lattice Data
作者:
RussellA. Boyles,
期刊:
Technometrics
(Taylor Available online 1996)
卷期:
Volume 38,
issue 1
页码: 37-49
ISSN:0040-1706
年代: 1996
DOI:10.1080/00401706.1996.10484414
出版商: Taylor & Francis Group
关键词: EM algorithm;Exploratory data analysis;Patterned covariance matrix;Positive definite estimation;Process capability analysis;Spatial correlation;Statistical process control
数据来源: Taylor
摘要:
Modern dimensional inspection techniques often produce more measurements per part than parts measured within reasonable timeframes. This poses a problem for multivariate process monitoring and capability analysis: The sample covariance matrix is not positive definite, hence not full rank and not invertible. When the measurement sites form a multidimensional lattice, spatially stationary covariance models provide positive definite estimates regardless of the number of measurements per part. I show that these estimates may be used in place of the sample covariance matrix to extend, and in some cases improve, standard multivariate methods. I describe a general class of lattices for which positive definite estimates are obtained via simple averaging or a closed-form EM algorithm. The proposed estimation and analysis procedures are illustrated in three case studies.
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