Literature DB >> 9385107

Estimating degradation by a Wiener diffusion process subject to measurement error.

G A Whitmore1.   

Abstract

Most materials and components degrade physically before they fail. Engineering degradation tests are designed to measure these degradation processes. Measurements in the tests reflect the inherent randomness of degradation itself as well as measurement errors created by imperfect instruments, procedures and environments. This paper describes a statistical model for measured degradation data that takes both sources of variation into account. The degradation process in the model is taken to be a Wiener diffusion process. The measurement errors are assumed to be independent normal random outcomes that are independent of the degradation process. The paper describes inference procedures for the model and discusses some practical issues that must be considered in dealing with the statistical problem. A case study is presented.

Mesh:

Year:  1995        PMID: 9385107     DOI: 10.1007/bf00985762

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  9 in total

1.  Estimation in degradation models with explanatory variables.

Authors:  V Bagdonavicius; M S Nikulin
Journal:  Lifetime Data Anal       Date:  2001-03       Impact factor: 1.588

2.  Some remarks on failure-times, surrogate markers, degradation, wear, and the quality of life.

Authors:  D R Cox
Journal:  Lifetime Data Anal       Date:  1999-12       Impact factor: 1.588

3.  Inference from accelerated degradation and failure data based on Gaussian process models.

Authors:  W J Padgett; Meredith A Tomlinson
Journal:  Lifetime Data Anal       Date:  2004-06       Impact factor: 1.588

4.  Covariates and random effects in a gamma process model with application to degradation and failure.

Authors:  Jerry Lawless; Martin Crowder
Journal:  Lifetime Data Anal       Date:  2004-09       Impact factor: 1.588

5.  Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Authors:  Chanseok Park; W J Padgett
Journal:  Lifetime Data Anal       Date:  2005-12       Impact factor: 1.588

6.  Parametric latent class joint model for a longitudinal biomarker and recurrent events.

Authors:  Jun Han; Elizabeth H Slate; Edsel A Peña
Journal:  Stat Med       Date:  2007-12-20       Impact factor: 2.373

7.  Failure inference from a marker process based on a bivariate Wiener model.

Authors:  G A Whitmore; M J Crowder; J F Lawless
Journal:  Lifetime Data Anal       Date:  1998       Impact factor: 1.588

8.  Modelling accelerated degradation data using Wiener diffusion with a time scale transformation.

Authors:  G A Whitmore; F Schenkelberg
Journal:  Lifetime Data Anal       Date:  1997       Impact factor: 1.588

9.  Parameter inference from hitting times for perturbed Brownian motion.

Authors:  Massimiliano Tamborrino; Susanne Ditlevsen; Peter Lansky
Journal:  Lifetime Data Anal       Date:  2014-09-04       Impact factor: 1.588

  9 in total

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