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Estimation of multiple-regime regressions with least absolutes deviation. (English) Zbl 0952.62021

Summary: This paper considers least absolute deviations estimation of a regression model with multiple change points occurring at unknown times. Some asymptotic results, including rates of convergence and asymptotic distributions, for the estimated change points and the estimated regression coefficient are derived. Results are obtained without assuming that each regime spans a positive fraction of the sample size. In addition, the number of change points is allowed to grow as the sample size increases. Estimation of the number of change points is also considered. A feasible computational algorithm is developed. An application is also given, along with some Monte Carlo simulations.

MSC:

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

Software:

Algorithm 478
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References:

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