Optimality Criteria for the Design of 2-Color Microarray Studies

Kerr, Kathleen F.

doi:10.1515/1544-6115.1583

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…H  as they stand, and not in their linear functions; cf. Kerr (2012). As hinted in the introduction, this exact design problem is combinatorially very complex, due to the nonorthogonality of the baseline parametrization.…”

Section: Baseline Parametrizationmentioning

confidence: 99%

Approximate theory-aided robust efficient factorial fractions under baseline parametrization

Mukerjee

Huda

2015

Ann Inst Stat Math

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With reference to a baseline parametrization, we explore highly efficient fractional factorial designs for inference on the main effects and, perhaps, some interactions. Our tools include approximate theory together with certain carefully devised discretization procedures. The robustness of these designs to possible model misspecification is investigated using a minimaxity approach. Examples are given to demonstrate that our technique works well even when the run size is quite small.

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Section: Baseline Parametrizationmentioning

confidence: 99%

Approximate theory-aided robust efficient factorial fractions under baseline parametrization

Mukerjee

Huda

2015

Ann Inst Stat Math

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“…Although our methods can be generalized to arbitrary block sizes, we focus on the important special case of experiments with blocks of size two (see the work of Godolphin). Such blocks occur routinely in microarray experiments and in experiments on people, for example, with eyes or arms as experimental units . Practical motivation for our work comes from a materials science experiment.…”

Section: Introductionmentioning

confidence: 99%

Copula‐based robust optimal block designs

Rappold

Müller

Woods

2019

Appl Stoch Models Bus & Ind

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Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modeling provides a computationally efficient approach for estimating population‐average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present a methodology for design selection, make comparisons to existing approaches in the literature, and assess the robustness of the designs to modeling assumptions.

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“…Although our methods can be generalized to arbitrary block sizes, we focus on the important special case of experiments with blocks of size two (see Godolphin, 2018). Such blocks occur routinely in microarray experiments (Bailey, 2007;Kerr, 2012) and in experiments on people, for example with eyes or arms as experimental units (David and Kempton, 1996). Practical motivation for our work comes from a materials science experiment.…”

Section: Introductionmentioning

confidence: 99%

Copula-based robust optimal block designs

Mueller¹,

Rappold²,

Woods³

2018

Preprint

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Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modelling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modelling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modelling provides a computationally efficient approach for estimating population-average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present methodology for design selection, make comparisons to existing approaches in the literature and assess the robustness of the designs to modelling assumptions.

show abstract

Optimality Criteria for the Design of 2-Color Microarray Studies

Cited by 7 publications

References 17 publications

Approximate theory-aided robust efficient factorial fractions under baseline parametrization

Approximate theory-aided robust efficient factorial fractions under baseline parametrization

Copula‐based robust optimal block designs

Copula-based robust optimal block designs

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