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Analysis of crack growth with robust, distribution-free estimators and tests for non-stationary autoregressive processes

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  • Christoph Kustosz
  • Christine Müller

Abstract

This article investigates the application of depth estimators to crack growth models in construction engineering. Many crack growth models are based on the Paris–Erdogan equation which describes crack growth by a deterministic differential equation. By introducing a stochastic error term, crack growth can be modeled by a non-stationary autoregressive process with Lévy-type errors. A regression depth approach is presented to estimate the drift parameter of the process. We then prove the consistency of the estimator under quite general assumptions on the error distribution. By an extension of the depth notion to simplical depth it is possible to use a degenerated U-statistic and to establish tests for general hypotheses about the drift parameter. Since the statistic asymptotically has a transformed $${\chi_1^2}$$ distribution, simple confidence intervals for the drift parameter can be obtained. In the second part, simulations of AR(1) processes with different error distributions are used to examine the quality of the constructed test. Finally we apply the presented method to crack growth experiments. We compare two datasets from independent experiments under different conditions but with the same material. We show that the parameter estimates differ significantly in this case. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Christoph Kustosz & Christine Müller, 2014. "Analysis of crack growth with robust, distribution-free estimators and tests for non-stationary autoregressive processes," Statistical Papers, Springer, vol. 55(1), pages 125-140, February.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:1:p:125-140
    DOI: 10.1007/s00362-012-0479-5
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    References listed on IDEAS

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    1. Wellmann, Robin & Müller, Christine H., 2010. "Tests for multiple regression based on simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 824-838, April.
    2. Wellmann, Robin & Müller, Christine H., 2010. "Depth notions for orthogonal regression," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2358-2371, November.
    3. Vygantas Paulaauskas & Svetlozar Rachev, 2003. "Maximum likelihood estimators in regression models with infinite variance innovations," Statistical Papers, Springer, vol. 44(1), pages 47-65, January.
    4. Lu Lin & Minghua Chen, 2006. "Robust estimating equation based on statistical depth," Statistical Papers, Springer, vol. 47(2), pages 263-278, March.
    5. Wellmann, Robin & Harmand, Peter & Müller, Christine H., 2009. "Distribution-free tests for polynomial regression based on simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 622-635, April.
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    Cited by:

    1. Christoph P. Kustosz & Anne Leucht & Christine H. MÜller, 2016. "Tests Based on Simplicial Depth for AR(1) Models With Explosion," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 763-784, November.

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