Wiener processes with random effects for degradation data

This article studies the maximum likelihood inference on a class of Wiener processes with random effects for degradation data. Degradation data are special case of functional data with monotone trend. The setting for degradation data is one on which n independent subjects, each with a Wiener process...

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Vydáno v:Journal of multivariate analysis Ročník 101; číslo 2; s. 340 - 351
Hlavní autor: Wang, Xiao
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Inc 01.02.2010
Elsevier
Taylor & Francis LLC
Edice:Journal of Multivariate Analysis
Témata:
62G20
62G20 62M05 62N05 Bootstrap Degradation EM algorithm Empirical processes Random effects Reliability Wiener process
62M05
62N05
Acceleration of convergence
Bootstrap
Bootstrap method
Degradation
Distribution theory
EM algorithm
Empirical processes
Exact sciences and technology
Mathematics
Maximum likelihood method
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Optimization algorithms
Parameter estimation
Parametric inference
Probability and statistics
Random effects
Reliability
Sciences and techniques of general use
Simulation
Statistics
Stochastic models
Studies
Wiener process
Degradation
62M05
62N05
62G20
Bootstrap
Random effects
Wiener process
Empirical processes
Reliability
EM algorithm
Empirical process
Statistical distribution
Multivariate analysis
Stochastic process
Asymptotic convergence
Industry
Hypothesis test
Engineering
Statistical test
Model matching
Convergence rate
Random effect
Jackknife method
Approximation theory
Convergence acceleration
Goodness of fit test
Asymptotic behavior
Statistical estimation
Life test
Functional
Statistical method
Survival analysis
Time distribution
Numerical analysis
Simulation
Trend analysis
Failure time
Maximum likelihood
Resampling method
ISSN:0047-259X, 1095-7243
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Popis
Shrnutí:This article studies the maximum likelihood inference on a class of Wiener processes with random effects for degradation data. Degradation data are special case of functional data with monotone trend. The setting for degradation data is one on which n independent subjects, each with a Wiener process with random drift and diffusion parameters, are observed at possible different times. Unit-to-unit variability is incorporated into the model by these random effects. EM algorithm is used to obtain the maximum likelihood estimators of the unknown parameters. Asymptotic properties such as consistency and convergence rate are established. Bootstrap method is used for assessing the uncertainties of the estimators. Simulations are used to validate the method. The model is fitted to bridge beam data and corresponding goodness-of-fit tests are carried out. Failure time distributions in terms of degradation level passages are calculated and illustrated.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2008.12.007