Optimum Experimental Designs, with SAS

By Anthony B. Atkinson, A. Donev, Randall D. Tobias. 2007

1,213 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6f72ba46660f15e19f9888e0f088aed831c784ba

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Citations in the corpus

Optimal subsampling for quantile regression in big data (2020, methodology): “ce also has an optimality interpretation in terms of optimal experimental design; it is termed the L-optimality criterion, where \L" stands for \linear transformation" of the estimator (see Atkinson et al., 2007). Using this criterion we are able to obtain the explicit expression of optima”
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].”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, methodology): “This transformation ensures that in a Bayesian information theoretic sense, the design that maximizes the DB-optimality criterion (7) also maximizes the expected Shannon information (Chaloner and Verdinelli 1995; Atkinson et al. 2007).”
A New Approach to Optimal Design for Linear Models With Correlated Observations (2010, background): “…errors, where numerous results and a rather complete theory are available (see, e.g., the monographs of Fedorov 1972; Silvey 1980; Pázman 1986; Atkinson, Donev, and Tobias 2007; or Pukelsheim 1993), the construction of optimal designs for dependent observations is intrinsically more difficult.”
Minimax and maximin space-filling designs: some properties and methods for construction (2017, unknown): “, Fedorov (1972), Silvey (1980), Pukelsheim (1993), Atkinson et al. (2007), Pronzato and Pázman (2013), Fedorov and Leonov (2014). The situation is much different for nonparametric regression with correlated errors, where the absence of prior knowledge on the mean and variance functions yields optim”
Absolutely maximally entangled states, combinatorial designs, and multiunitary matrices (2015, unknown): “Furthermore, they are a fundamental tool in the optimal design of experiments [43,44].”
Simulation-based optimal Bayesian experimental design for nonlinear systems (2011, background): “ing applications. When observables depend linearly on parameters of interest, common solution criteria for the optimal experimental design problem are written as functionals of the information matrix [1]. These criteria include the well-known ‘alphabetic optimality’ conditions, e.g., A-optimality to”