Monotonic convergence of a general algorithm for computing optimal designs
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TLDR
In this paper, the authors established monotonic convergence for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. 14 (1978) 1379−1389] for computing optimal designs.Abstract:
Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. Theory Methods 14 (1978) 1379–1389] for computing optimal designs. A conjecture of Titterington [Appl. Stat. 27 (1978) 227–234] is confirmed as a consequence. Optimal designs for logistic regression are used as an illustration.read more
Citations
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SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.
Journal ArticleDOI
New Method of Sparse Parameter Estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: A new semiparametric/sparse method is introduced, called SPICE, which is computationally quite efficient, enjoys global convergence properties, can be readily used in the case of replicated measurements and, unlike most other sparse estimation methods, does not require any subtle choices of user parameters.
Journal ArticleDOI
On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm
TL;DR: This work presents a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multistage design strategies.
Journal ArticleDOI
D-optimal designs via a cocktail algorithm
TL;DR: In this article, a fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs, the cocktail algorithm, which extends the well-known vertex direction method (VDM) and the multiplicative algorithm.
Journal ArticleDOI
D-optimal designs via a cocktail algorithm
TL;DR: A fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs that extends the well-known vertex direction method and the multiplicative algorithm, and shares their simplicity and monotonic convergence properties.
References
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TL;DR: This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models, and presents a uniied view of the topic by putting experimental design in a decision theoretic framework.
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Optimal Design of Experiments
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The Equivalence of Two Extremum Problems
J. Kiefer,J. Wolfowitz +1 more
TL;DR: In this article, the authors consider the problem of defining probability measures with finite support, i.e., measures that assign probability one to a set consisting of a finite number of points.
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General Equivalence Theory for Optimum Designs (Approximate Theory)
TL;DR: For general optimality criteria, this article obtained criteria equivalent to $\Phi$-optimality under various conditions on ''Phi'' and showed that such equivalent criteria are useful for analytic or machine computation of ''phi''-optimum designs.