The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

H. J. Ramaker; E. N. M. van Sprang; J. A. Westerhuis; A. K. Smilde

doi:10.1016/j.chemolab.2003.12.015

The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

H. J. Ramaker
, E. N. M. van Sprang
, J. A. Westerhuis
, A. K. Smilde

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

This paper describes the sensitivity of false alarm rate to misspecification of the number of PCA components in multivariate statistical process control (MSPC) models. Using simulations, it is shown that choosing an incorrect number of components in monitoring models may seriously affect the false alarm rates of the control charts. Furthermore, the false alarm rate becomes worrysome when the size of the training set is small. Using a leave-one-out procedure for building the control charts partly solves this problem. (C) 2004 Published by Elsevier B.V

Original language	English
Pages (from-to)	181-187
Journal	CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume	73
Issue number	2
DOIs	https://doi.org/10.1016/j.chemolab.2003.12.015
Publication status	Published - 2004

Access to Document

10.1016/j.chemolab.2003.12.015

Cite this

@article{f05da7108e0143d49a4f51fea0d15b5b,

title = "The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring",

abstract = "This paper describes the sensitivity of false alarm rate to misspecification of the number of PCA components in multivariate statistical process control (MSPC) models. Using simulations, it is shown that choosing an incorrect number of components in monitoring models may seriously affect the false alarm rates of the control charts. Furthermore, the false alarm rate becomes worrysome when the size of the training set is small. Using a leave-one-out procedure for building the control charts partly solves this problem. (C) 2004 Published by Elsevier B.V",

author = "Ramaker, \{H. J.\} and \{van Sprang\}, \{E. N. M.\} and Westerhuis, \{J. A.\} and Smilde, \{A. K.\}",

year = "2004",

doi = "10.1016/j.chemolab.2003.12.015",

language = "English",

volume = "73",

pages = "181--187",

journal = "CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS",

issn = "0169-7439",

publisher = "Elsevier",

number = "2",

}

TY - JOUR

T1 - The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

AU - Ramaker, H. J.

AU - van Sprang, E. N. M.

AU - Westerhuis, J. A.

AU - Smilde, A. K.

PY - 2004

Y1 - 2004

N2 - This paper describes the sensitivity of false alarm rate to misspecification of the number of PCA components in multivariate statistical process control (MSPC) models. Using simulations, it is shown that choosing an incorrect number of components in monitoring models may seriously affect the false alarm rates of the control charts. Furthermore, the false alarm rate becomes worrysome when the size of the training set is small. Using a leave-one-out procedure for building the control charts partly solves this problem. (C) 2004 Published by Elsevier B.V

AB - This paper describes the sensitivity of false alarm rate to misspecification of the number of PCA components in multivariate statistical process control (MSPC) models. Using simulations, it is shown that choosing an incorrect number of components in monitoring models may seriously affect the false alarm rates of the control charts. Furthermore, the false alarm rate becomes worrysome when the size of the training set is small. Using a leave-one-out procedure for building the control charts partly solves this problem. (C) 2004 Published by Elsevier B.V

U2 - 10.1016/j.chemolab.2003.12.015

DO - 10.1016/j.chemolab.2003.12.015

M3 - Article

SN - 0169-7439

VL - 73

SP - 181

EP - 187

JO - CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

JF - CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

IS - 2

ER -

The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

Abstract

Access to Document

Fingerprint

Cite this