Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search

DOI10.1016/j.apm.2019.03.012zbMath1481.62062OpenAlexW2921963090MaRDI QIDQ1984867

Min Xu, Guoqi Qian, Yuehua Wu

Publication date: 7 April 2020

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2019.03.012

zbMATH Keywords

model selectionGibbs samplerchange-pointsBayesian information criteria

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian problems; characterization of Bayes procedures (62C10) Numerical analysis or methods applied to Markov chains (65C40) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (2)

Statistical analysis of the non-stationary binomial AR(1) model with change point ⋮ Asymptotic properties of \(M\)-estimators based on estimating equations and censored data in semi-parametric models with multiple change points

Uses Software

• DNAcopy

Cites Work

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• Using penalized contrasts for the change-point problem
• Model selection criteria in multivariate models with multiple structural changes
• Simultaneous change point analysis and variable selection in a regression problem
• On time series model selection involving many candidate ARMA models
• Estimating the number of change-points via Schwarz' criterion
• Estimating the dimension of a model
• Discretization and MCMC. Convergence assessment
• Convergence of a stochastic approximation version of the EM algorithm
• Computations and analysis in robust regression model
• Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo
• Detection of multiple change-points in multivariate time series
• A Statistical Test of Change-Point in Mean that Almost Surely Has Zero Error Probabilities
• Structural breaks in time series
• A test for a change in a parameter occurring at an unknown point
• Testing a Sequence of Observations for a Shift in Location
• Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance
• Testing and Locating Variance Changepoints with Application to Stock Prices
• Estimating and Testing Linear Models with Multiple Structural Changes
• A Bayesian Analysis for Change Point Problems
• Circular binary segmentation for the analysis of array-based DNA copy number data
• Structural Break Estimation for Nonstationary Time Series Models
• An application of MCMC methods for the multiple change-points problem.
• Gaussian model selection

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