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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Veröffentlicht in:	Journal of multivariate analysis Jg. 101; H. 2; S. 340 - 351
1. Verfasser:	Wang, Xiao
Format:	Journal Article Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Inc 01.02.2010 Elsevier Taylor & Francis LLC
Schriftenreihe:	Journal of Multivariate Analysis
Schlagworte:	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
Online-Zugang:	Volltext
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