Statistics for Experimenters: Design, Innovation and Discovery

By Joseph G. Pigeon. Technometrics, 2006

1,078 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c68cd6754f6b8bbab314638f27802956ef683f0f

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Practical guide to controlled experiments on the web: listen to your customers not to the hippo (2007, methodology): “We will not review the details of the statistical tests, as they are described very well in many statistical books (15; 16; 6).”
Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting (2018, background): “that with moderately large |Mk,D|L, the binomial random variable B(|Mk,D|L, pD) defined in Proposition 1 can be well approximated by Gaussian random variable N (μ, v2) [39], where μ = |Mk,D|LpD and v2 = |Mk,D|LpD(1 − pD).”
Nanoemulsions of β-carotene using a high-energy emulsification–evaporation technique (2011, methodology): “Experimental design 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 2(3) with a central point (Box et al., 2005) (see Table 1).”
ISOMORPHIC PERMUTED EXPERIMENTAL DESIGNS AND THEIR APPLICATION IN CONJOINT ANALYSIS (2010, background): “Box et al. (2005) compares a range of experimental designs varying in the number of variables (factors) (applied to attributes in conjoint analysis), and the number of levels (matched to the elements in conjoint analysis) combined into runs (experimental units or rows of design) (matched to…”
Least squares polynomial chaos expansion: A review of sampling strategies (2017, background): “Although in the context of least squares regression – as opposed to least squares approximation (see Section 4.5.2) – a selection of input realizations for building polynomial models with certain optimality goal has been expensively studied over the last few decades; see, e.g., [36, 37, 9, 95, 3].”
Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2017, background): “This is why, in the theory of experimental design (Box et al., 2005) factors are moved in groups, rather than OAT, to optimize the exploration of the space of the factors.”
The Salience of Social Contextual Factors in Appraisals of Police Interactions with Citizens: A Randomized Factorial Experiment (2014, methodology): “A factorial experiment is a research design that consists of two or more factors that have discrete possible values or levels where the experimental units of analysis are distributed across all possible combinations of these levels (see Fisher 1926; Box et al. 2005).”