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Optimal design when outcome values are not missing at random

Optimal design when outcome values are not missing at random
Optimal design when outcome values are not missing at random
The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song and Wong (2002) develop such a framework. However, when data are not missing at random this framework can lead to inefficient designs. We investigate and address the specific challenges that not missing at random values present when finding optimal designs for linear regression models. We show that the optimality criteria depend on model parameters that traditionally do not affect the design, such as regression coefficients and the residual variance. We also develop a framework that improves efficiency of designs over those found when values are missing at random.
missing observations, not missing at random, optimal design
1017-0405
1821-1838
Lee, Kim
526cb8f4-17e8-40e3-881a-19e5eacabd23
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Lee, Kim
526cb8f4-17e8-40e3-881a-19e5eacabd23
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039

Lee, Kim, Mitra, Robin and Biedermann, Stefanie (2018) Optimal design when outcome values are not missing at random. Statistica Sinica, 28 (4), 1821-1838. (doi:10.5705/ss.202016.0526).

Record type: Article

Abstract

The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song and Wong (2002) develop such a framework. However, when data are not missing at random this framework can lead to inefficient designs. We investigate and address the specific challenges that not missing at random values present when finding optimal designs for linear regression models. We show that the optimality criteria depend on model parameters that traditionally do not affect the design, such as regression coefficients and the residual variance. We also develop a framework that improves efficiency of designs over those found when values are missing at random.

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MNAR_r_final0317 - Accepted Manuscript
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Accepted/In Press date: 26 March 2017
e-pub ahead of print date: 17 September 2018
Keywords: missing observations, not missing at random, optimal design
Organisations: Statistics, Mathematical Sciences

Identifiers

Local EPrints ID: 407201
URI: https://eprints.soton.ac.uk/id/eprint/407201
ISSN: 1017-0405
PURE UUID: bf114a33-d075-446e-89d7-c23e4c578ae9

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Date deposited: 01 Apr 2017 01:05
Last modified: 14 Mar 2019 06:02

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Contributors

Author: Kim Lee
Author: Robin Mitra

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