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Dealing with observational data in control

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Dealing with observational data in control. / Wilson, Emma D.; Clairon, Q.; Henderson, R. et al.
In: Annual Reviews in Control, Vol. 46, 2018, p. 94-106.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wilson, ED, Clairon, Q, Henderson, R & Taylor, CJ 2018, 'Dealing with observational data in control', Annual Reviews in Control, vol. 46, pp. 94-106. https://doi.org/10.1016/j.arcontrol.2018.05.001

APA

Wilson, E. D., Clairon, Q., Henderson, R., & Taylor, C. J. (2018). Dealing with observational data in control. Annual Reviews in Control, 46, 94-106. https://doi.org/10.1016/j.arcontrol.2018.05.001

Vancouver

Wilson ED, Clairon Q, Henderson R, Taylor CJ. Dealing with observational data in control. Annual Reviews in Control. 2018;46:94-106. Epub 2018 May 29. doi: 10.1016/j.arcontrol.2018.05.001

Author

Wilson, Emma D. ; Clairon, Q. ; Henderson, R. et al. / Dealing with observational data in control. In: Annual Reviews in Control. 2018 ; Vol. 46. pp. 94-106.

Bibtex

@article{3e454b68a77e491db0db69b89f9ab45b,
title = "Dealing with observational data in control",
abstract = "There is growing interest in the use of control theory for interdisciplinary applications, where data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and where there is no opportunity to collect repeat measurements. In such applications, problems posed by observational data and the issue of missing or irregular data need to be considered. We present a review on dealing with observational, missing and irregular data for control applications. This considers the following issues: i) how to identify a system model from observational data subject to missing measurements, ii) how to determine control inputs when output data includes missing measurements, and iii) how to ensure stability when future update times may be missed. Dealing with observational data and missing measurements is a key problem within the statistics literature, so we introduce statistical methods for dealing with this type of data. We aim to enable the integration of well-developed statistical methods of dealing with missing data into control theory. An example problem of using anticoagulants to control the blood clotting speed of patients is used throughout the paper.",
keywords = "interdisciplinarity, observational data, causation, system identification, missing measurements, missing data, longitudinal data, periodic control, irregular control, event driven control",
author = "Wilson, {Emma D.} and Q. Clairon and R. Henderson and Taylor, {C. James}",
year = "2018",
doi = "10.1016/j.arcontrol.2018.05.001",
language = "English",
volume = "46",
pages = "94--106",
journal = "Annual Reviews in Control",
issn = "1367-5788",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Dealing with observational data in control

AU - Wilson, Emma D.

AU - Clairon, Q.

AU - Henderson, R.

AU - Taylor, C. James

PY - 2018

Y1 - 2018

N2 - There is growing interest in the use of control theory for interdisciplinary applications, where data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and where there is no opportunity to collect repeat measurements. In such applications, problems posed by observational data and the issue of missing or irregular data need to be considered. We present a review on dealing with observational, missing and irregular data for control applications. This considers the following issues: i) how to identify a system model from observational data subject to missing measurements, ii) how to determine control inputs when output data includes missing measurements, and iii) how to ensure stability when future update times may be missed. Dealing with observational data and missing measurements is a key problem within the statistics literature, so we introduce statistical methods for dealing with this type of data. We aim to enable the integration of well-developed statistical methods of dealing with missing data into control theory. An example problem of using anticoagulants to control the blood clotting speed of patients is used throughout the paper.

AB - There is growing interest in the use of control theory for interdisciplinary applications, where data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and where there is no opportunity to collect repeat measurements. In such applications, problems posed by observational data and the issue of missing or irregular data need to be considered. We present a review on dealing with observational, missing and irregular data for control applications. This considers the following issues: i) how to identify a system model from observational data subject to missing measurements, ii) how to determine control inputs when output data includes missing measurements, and iii) how to ensure stability when future update times may be missed. Dealing with observational data and missing measurements is a key problem within the statistics literature, so we introduce statistical methods for dealing with this type of data. We aim to enable the integration of well-developed statistical methods of dealing with missing data into control theory. An example problem of using anticoagulants to control the blood clotting speed of patients is used throughout the paper.

KW - interdisciplinarity

KW - observational data

KW - causation

KW - system identification

KW - missing measurements

KW - missing data

KW - longitudinal data

KW - periodic control

KW - irregular control

KW - event driven control

U2 - 10.1016/j.arcontrol.2018.05.001

DO - 10.1016/j.arcontrol.2018.05.001

M3 - Journal article

VL - 46

SP - 94

EP - 106

JO - Annual Reviews in Control

JF - Annual Reviews in Control

SN - 1367-5788

ER -