Dimension-reduced nonparametric maximum likelihood computation for interval-censored data
Computational Statistics & Data Analysis
Improving updating rules in multiplicative algorithms for computing D-optimal designs
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computing efficient exact designs of experiments using integer quadratic programming
Computational Statistics & Data Analysis
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A fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs. This cocktail algorithm extends the well-known vertex direction method (VDM; Fedorov in Theory of Optimal Experiments, 1972) and the multiplicative algorithm (Silvey et al. in Commun. Stat. Theory Methods 14:1379---1389, 1978), and shares their simplicity and monotonic convergence properties. Numerical examples show that the cocktail algorithm can lead to dramatically improved speed, sometimes by orders of magnitude, relative to either the multiplicative algorithm or the vertex exchange method (a variant of VDM). Key to the improved speed is a new nearest neighbor exchange strategy, which acts locally and complements the global effect of the multiplicative algorithm. Possible extensions to related problems such as nonparametric maximum likelihood estimation are mentioned.