Statistics for Experimenters: Design, Innovation, and Discovery

By Terry Speed. 2006

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Among them, Full Factorial, Fractional Factorial (Box et al., 2005), Central Composite (Myers, 1971), BoxeBehnken (Box and Behnken, 1960), Monte Carlo, Latin Hypercube (McKay et al., 1979) and QuasiMonte Carlo (QMC) (Sobol',1967; Halton,1964) designs are themost widely used.”
Causal inference from 2K factorial designs by using potential outcomes (2015, methodology): “…Neymanian inference on individual or a collection of factorial effects under the framework proposed is essentially the same as the linear-model-based inference that has been widely used by experimenters and analysts for decades (see, for example, Wu and Hamada (2011) and Box et al. (2005)).”
Nanoemulsions of β-carotene using a high-energy emulsification–evaporation technique (2011, methodology): “In order to evaluate the high-energy emulsion–evaporation technique for production and storage stability of b-carotene nanodispersions, these structures were prepared as described above, following a full factorial design 23 with a central point (Box et al., 2005) (see Table 1).”
Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua (2014, methodology): “A central composite design (CCD) was selected to determine he experimental conditions as the inclusion of axial experimenal points allow for a larger spread of conditions to be examined, hich is beneficial when the required complexity of model is not nown for accurate predictions to be made (Box et ”
Robust Estimation of Inverse Probability Weights for Marginal Structural Models (2015, methodology): “Within each time period, row vectors of +’s and −’s for the treatment history combinations are orthogonal to one another.”
Verification and validation benchmarks (2008, methodology): “procedure for estimating uncertainty; propagation of contributing uncertainties to formally estimate uncertainty in an SRQ [84]; and statistical design-of-experiment procedures to directly estimate the uncertainty in SRQs using multiple realizations of the experimental measurements under varying con”
Rerandomization and regression adjustment (2019, methodology): “es on average, and serves as a basis for statistical inference. Since then, randomized experiments have been widely used in agricultural sciences (e.g., Fisher 1935; Kempthorne 1952), industry (e.g., Box et al. 2005; Wu and Hamada 2011), and clinical trials (e.g., Rosenberger and Lachin 2015). Recen”