d-QPSO: A Quantum-Behaved Particle Swarm Technique for FindingD-Optimal Designs With Discrete and Continuous Factors and a Binary Response

DOI10.1080/00401706.2018.1439405MaRDI QIDQ6621622

Weng Kee Wong, Joshua Lukemire, Abhyuday Mandal

Publication date: 18 October 2024

Published in: Technometrics (Search for Journal in Brave)

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• D-optimal designs via a cocktail algorithm
• Minimax and Minimax Projection Designs Using Clustering
• Minimax optimal designs via particle swarm optimization methods
• Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine
• Optimizing Latin hypercube designs by particle swarm
• Robustness against separation and outliers in logistic regression
• Model selection via Bayesian information capacity designs for generalised linear models
• Design selection criteria for discrimination/estimation for nested models and a binomial re\-sponse
• Designs for generalized linear models
• Algorithmic searches for optimal designs
• Optimal designs for the Michaelis–Menten model with correlated observations
• Optimal designs for generalized linear models with multiple design variables
• Optimum Designs in Regression Problems
• Optimal designs for 2k factorial experiments with binary response
• Sequential Experimental Designs for Generalized Linear Models
• Numerical Optimization
• Bias reduction of maximum likelihood estimates

Related Items (3)

Particle swarm optimization for searching efficient experimental designs: a review ⋮ Optimal designs for nonlinear mixed-effects models using competitive swarm optimizer with mutated agents ⋮ ForLion: a new algorithm for D-optimal designs under general parametric statistical models with mixed factors

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