The S-Estimator in Change-Point Random Model with Long Memory

2009/06/09 by Gabriela Ciuperca, Ciuperca, Gabriela
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH

paper · pdf · doi:10.48550/arxiv.0906.1710

arxiv created 2009/06/09 · openalex publication_date 2009/06/09 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The paper considers two-phase random design linear regression models. The errors and the regressors are stationary long-range dependent Gaussian. The regression parameters, the scale parameters and the change-point are estimated using a method introduced by Rousseeuw and Yohai(1984). This is called S-estimator and it has the property that is more robust than the classical estimators; the outliers don't spoil the estimation results. Some asymptotic results, including the strong consistency and the convergence rate of the S-estimators, are proved.

Citations

• Estimating and Testing Linear Models with Multiple Structural Changes
• The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data

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