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A Comparison of Criteria to Design Efficient Choice Experiments
To date, no attempt has been made to design efficient choice experiments by means of the G- and V-optimality criteria. These criteria are known to make precise response predictions, which is exactlyExpand
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A Mixed Integer Programming Model for Solving a Layout Problem in the Fashion Industry
The cutting operation in the high fashion clothing industry essentially involves putting several layers of cloth on a long cutting table and fixing templates of the parts of several articles on topExpand
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Outperforming Completely Randomized Designs
Split-plot designs have become increasingly popular in industrial experimentation because some of the factors under investigation are often hard-to-change. It is well-known that the resultingExpand
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An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs
Recently, Kessels et al. (2006) developed a way to produce Bayesian G- and V-optimal designs for the multinomial logitmodel. These designs allow for precise response predictions which is the goal ofExpand
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D-Optimal Split-Plot Designs With Given Numbers and Sizes of Whole Plots
TLDR
We provide an efficient algorithm to compute D-optimal split-plot designs with given numbers of whole plots and given whole-plot sizes. Expand
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Quantifying benefit-risk preferences for new medicines in rare disease patients and caregivers
BackgroundRare disease patients and caregivers face uncommon, serious, debilitating conditions often characterised by poor prognosis and limited treatment options. This study aimed to explore whatExpand
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Models and Optimal Designs for Conjoint Choice Experiments Including a No-Choice Option
In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life the respondent does not always make a choice: oftenExpand
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Practical Inference from Industrial Split-Plot Designs
In many industrial response surface experiments, some of the factors investigated are not reset independently. The resulting experimental design then is of the split-plot type, and the observationsExpand
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The usefulness of Bayesian optimal designs for discrete choice experiments
TLDR
We show using an extensive case study that the resulting utility‐neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Expand
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A comparison of variational approximations for fast inference in mixed logit models
TLDR
Variational Bayesian methods aim to address some of the weaknesses (computation time, storage costs and convergence monitoring) of mainstream Markov chain Monte Carlo based inference at the cost of a biased but more tractable approximation to the posterior distribution. Expand
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