Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm
From MaRDI portal
Publication:2184405
DOI10.1007/s00180-019-00906-xzbMath1505.62372OpenAlexW2950141511MaRDI QIDQ2184405
Publication date: 28 May 2020
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-019-00906-x
simulated annealingMarkov chain Monte Carlo (MCMC)half-space depth computationmultiple try Metropolis
Computational methods for problems pertaining to statistics (62-08) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Monte Carlo methods (65C05)
Related Items (3)
Uniform convergence rates for the approximated halfspace and projection depth ⋮ Flexible integrated functional depths ⋮ Robustness of the deepest projection regression functional
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Fast nonparametric classification based on data depth
- Optimization by Simulated Annealing
- On robust classification using projection depth
- A multi-point Metropolis scheme with generic weight functions
- Interacting multiple try algorithms with different proposal distributions
- The random Tukey depth
- Exact computation of the halfspace depth
- Fast implementation of the Tukey depth
- Multiple-try simulated annealing algorithm for global optimization
- General notions of statistical depth function.
- Simulated annealing for higher dimensional projection depth
- Absolute approximation of Tukey depth: theory and experiments
- Finite sample breakdown point of Tukey's halfspace median
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Regression Depth
- The Multiple-Try Method and Local Optimization in Metropolis Sampling
- A Limited Memory Algorithm for Bound Constrained Optimization
- Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule
- Equation of State Calculations by Fast Computing Machines
- Simulated annealing for the bounds of Kendall's τ and Spearman's ρ
- Computing Halfspace Depth and Regression Depth
- Monte Carlo sampling methods using Markov chains and their applications
- A Simplex Method for Function Minimization
- Methods of conjugate gradients for solving linear systems
- Monte Carlo strategies in scientific computing
This page was built for publication: Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm