Wiener processes with random effects for degradation data

• Published in: Journal of multivariate analysis Vol. 101; no. 2; pp. 340 - 351
• Main Author: Wang, Xiao
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2010; Elsevier; Taylor & Francis LLC
• Series: Journal of Multivariate Analysis
• Subjects: 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

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...