Estimation of multiple-regime regressions with least absolutes deviation

DOI10.1016/S0378-3758(98)00082-2zbMath0952.62021OpenAlexW2105675672MaRDI QIDQ1298916

Jushan Bai

Publication date: 11 January 2001

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(98)00082-2

zbMATH Keywords

asymptotic distributionleast absolute deviationmultiple change pointsmultiple-regime regressions

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Robustness and adaptive procedures (parametric inference) (62F35) Discrete-time Markov processes on general state spaces (60J05)

Related Items (24)

\(\tau\)-estimators of regression models with structural change of unknown location ⋮ Multiple change-points estimation in linear regression models via an adaptive Lasso expectile loss function ⋮ Rank-based multiple change-point detection ⋮ Robust change point detection method via adaptive LAD-Lasso ⋮ A quasi-Bayesian change point detection with exchangeable weights ⋮ Adaptive LASSO model selection in a multiphase quantile regression ⋮ Laplace's method and BIC model selection for least absolute value criterion ⋮ Data-driven selection of the number of change-points via error rate control ⋮ Group fused Lasso for large factor models with multiple structural breaks ⋮ Penalized least absolute deviations estimation for nonlinear model with change-points ⋮ SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES ⋮ Estimating nonlinear regression with and without change-points by the LAD method ⋮ A linear regression model with persistent level shifts: an alternative to infill asymptotics ⋮ Testing for common breaks in a multiple equations system ⋮ Robust algorithms for multiphase regression models ⋮ Consistent selection of the number of change-points via sample-splitting ⋮ Estimating structural changes in regression quantiles ⋮ Model selection by LASSO methods in a change-point model ⋮ Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle ⋮ Robust change point estimation in two-phase linear regression models: an application to metabolic pathway data ⋮ Empirical likelihood for nonlinear models with missing responses ⋮ Inference for single and multiple change-points in time series ⋮ The S-estimator in the change-point random model with long memory ⋮ The likelihood ratio method for testing changes in the parameters of double exponential observations

Uses Software

• Algorithm 478

Cites Work

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• Estimating the number of change-points via Schwarz' criterion
• Maximum likelihood estimation of a change-point in the distribution of independent random variables: general multiparameter case
• Strong representations for LAD estimators in linear models
• The asymptotic behavior of some nonparametric change-point estimators
• Limit theorems for change in linear regression
• A priori estimates in problems of ``change-points of a random sequence.
• Estimation of a change in linear models
• Nonparametric estimation in change-point models
• Testing and estimating change-points in time series
• Detection of the number, locations and magnitudes of jumps
• The likelihood ratio test for a change-point in simple linear regression
• Partition Regression
• Asymptotic Theory of Least Absolute Error Regression
• LEAST SQUARES ESTIMATION OF A SHIFT IN LINEAR PROCESSES
• Estimators for the Time of Change in Linear Models
• Estimating and Testing Linear Models with Multiple Structural Changes
• Detecting Changes in Linear Regressions
• The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes

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