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Robust nonparametric regression in time series

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  • Truong, Young K.

Abstract

Consider a stationary time series (Xt, Yt), t = 0, ±1, ... with Xt being d-valued and Yt real-valued. Let [psi](·) denote a monotone function and let [theta](·) denote the robust conditional location functional so that E[[psi](Y0 - [theta](X0))X0] = 0. Given a finite realization (X1, Y1), ..., (Xn, Yn), the problem of estimating [theta](·) is considered. Under appropriate regularity conditions, it is shown that a sequence of the robust conditional location functional estimators can be chosen to achieve the optimal rate of convergence n-1/(2 + d) both pointwise and in Lq (1

Suggested Citation

  • Truong, Young K., 1992. "Robust nonparametric regression in time series," Journal of Multivariate Analysis, Elsevier, vol. 41(2), pages 163-177, May.
    • Handle: RePEc:eee:jmvana:v:41:y:1992:i:2:p:163-177
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    Cited by:

    1. Cai, Zongwu, 2003. "Nonparametric estimation equations for time series data," Statistics & Probability Letters, Elsevier, vol. 62(4), pages 379-390, May.
    2. Peng, Liang & Yao, Qiwei, 2004. "Nonparametric regression under dependent errors with infinite variance," LSE Research Online Documents on Economics 22874, London School of Economics and Political Science, LSE Library.

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