Simulated annealing for higher dimensional projection depth

DOI10.1016/j.csda.2012.05.002zbMath1255.62188OpenAlexW2029889932MaRDI QIDQ1927183

Wei Shao, YiJun Zuo

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.05.002

zbMATH Keywords

Markov chain Monte Carloapproximate algorithm

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Nonparametric estimation (62G05) Approximation methods and heuristics in mathematical programming (90C59) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (6)

Approximate computation of projection depths ⋮ Employing the MCMC technique to compute the projection depth in high dimensions ⋮ Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm ⋮ Uniform convergence rates for the approximated halfspace and projection depth ⋮ Multiple-try simulated annealing algorithm for global optimization ⋮ Simulated annealing for the bounds of Kendall's τ and Spearman's ρ

Cites Work

• Optimization by Simulated Annealing
• On robust classification using projection depth
• A resampling design for computing high-breakdown regression
• Exact computation of bivariate projection depth and the Stahel-Donoho estimator
• Projection-based depth functions and associated medians
• General notions of statistical depth function.
• Structural properties and convergence results for contours of sample statistical depth functions.
• On the Stahel-Donoho estimator and depth-weighted means of multivariate data.
• Data depths satisfying the projection property
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Behavior of the Stahel-Donoho Robust Multivariate Estimator
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• Monte Carlo strategies in scientific computing
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