Pure error REML for analyzing data from multi-stratum designs

Since the dawn of response surface methodology, it has been recommended that designs include replicate points, so that pure error estimates of variance can be obtained and used to provide reliable estimated standard errors of the effects of factors. In designs with more than one stratum, such as split-plot and split-split-plot designs, it is less obvious how pure error estimates of the variance components should be obtained, and no pure error estimates are given by the popular residual maximum likelihood (REML) method of estimation. A method of pure error REML estimation of the variance components, using the full treatment model, is obtained by treating each combination of factor levels as a discrete treatment. This method is easy to implement using standard software and improved estimated standard errors of the fixed effects estimates can be obtained by applying the Kenward-Roger correction based on the pure error REML estimates. The new method is illustrated using several data sets and the performance of pure error REML is compared with the standard REML method. The results are comparable when the assumed response surface model is correct, but the new method is considerably more robust in the case of model misspecification.

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Publication Details

Journal
Computational Statistics & Data Analysis
Published
2025-12-25
DOI
https://doi.org/10.1016/j.csda.2025.108322
Primary Topic
Optimal Experimental Design Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Pure error REML for analyzing data from multi-stratum designs

Peter Goos, Steven G. Gilmour, Heiko Großmann
Computational Statistics & Data Analysis
Optimal Experimental Design Methods
article

Pure error REML for analyzing data from multi-stratum designs

Peter Goos, Steven G. Gilmour, Heiko Großmann
article en

Abstract

Since the dawn of response surface methodology, it has been recommended that designs include replicate points, so that pure error estimates of variance can be obtained and used to provide reliable estimated standard errors of the effects of factors. In designs with more than one stratum, such as split-plot and split-split-plot designs, it is less obvious how pure error estimates of the variance components should be obtained, and no pure error estimates are given by the popular residual maximum likelihood (REML) method of estimation. A method of pure error REML estimation of the variance components, using the full treatment model, is obtained by treating each combination of factor levels as a discrete treatment. This method is easy to implement using standard software and improved estimated standard errors of the fixed effects estimates can be obtained by applying the Kenward-Roger correction based on the pure error REML estimates. The new method is illustrated using several data sets and the performance of pure error REML is compared with the standard REML method. The results are comparable when the assumed response surface model is correct, but the new method is considerably more robust in the case of model misspecification.

Computational Statistics & Data AnalysisVol. 218
King's College London (GB), Otto-von-Guericke-Universität Magdeburg (DE), KU Leuven (BE)
Engineering and Physical Sciences Research Council, Division of Mathematical Sciences
Openalex Percentile: Top 28%
Optimal Experimental Design Methods
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