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

DOI10.1007/s00362-012-0479-5zbMath1298.62202OpenAlexW2052056359MaRDI QIDQ2442687

Christoph P. Kustosz, Christine H. Müller

Publication date: 1 April 2014

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-012-0479-5

zbMATH Keywords

stochastic differential equationrobustnessconfidence intervalsnon-stationary processescrack growthtestsautoregressive processmaximum depth estimatorsimplical depth

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric robustness (62G35) Nonparametric estimation (62G05) Applications of statistics in engineering and industry; control charts (62P30) Interacting random processes; statistical mechanics type models; percolation theory (60K35)

Related Items (2)

Tests Based on Simplicial Depth for AR(1) Models With Explosion ⋮ Simplified simplicial depth for regression and autoregressive growth processes

Uses Software

• sde

Cites Work

• Depth estimators and tests based on the likelihood principle with application to regression
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• Numerical solution of SDE through computer experiments. Including floppy disk
• Maximum likelihood estimators in regression models with infinite variance innovations
• On depth and deep points: A calculus.
• Tests for multiple regression based on simplicial depth
• Simulation and inference for stochastic differential equations. With R examples.
• On Asymptotic Distributions of Estimates of Parameters of Stochastic Difference Equations
• Nonparametric prediction intervals for explosive ar(1)-processes
• THE SIGN TEST FOR STOCHASTIC PROCESSES
• On a notion of simplicial depth
• Regression Depth
• On the Statistical Treatment of Linear Stochastic Difference Equations

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