An Inverse Gaussian Process Model for Degradation Data

• Published in: Technometrics Vol. 52; no. 2; pp. 188 - 197
• Main Authors: Wang, Xiao, Xu, Dihua
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.05.2010; The American Society for Quality and The American Statistical Association; American Society for Quality and the American Statistical Association; American Society for Quality
• Subjects: Applied sciences; Biodegradation; Bootstrap; Bootstrap resampling; Computer science; control theory; systems; Data models; Data processing. List processing. Character string processing; Degradation data; EM algorithm; Empirical processes; Estimation methods; Exact sciences and technology; Gaussian distributions; Lasers; Material degradation; Mathematics; Maximum likelihood estimation; Memory organisation. Data processing; Modeling; Nonparametric models; Operational research and scientific management; Operational research. Management science; Parametric inference; Probability and statistics; Random effects; Reliability; Reliability theory. Replacement problems; Sciences and techniques of general use; Software; Statistics; Data analysis; Empirical process; Goodness of fit test; Variability; Random effects; Covariate; Empirical processes; Modeling; Bootstrapping; Time distribution; Heterogeneity; Statistical test; Degradation data; Gaussian process; Break up time; Remolded sample; Laser; Gaussian processes; Bootstrap; Random effect; Data models; Maximum likelihood; Reliability; EM algorithm

This paper studies the maximum likelihood estimation of a class of inverse Gaussian process models for degradation data. Both the subject-to-subject heterogeneity and covariate information can be incorporated into the model in a natural way. The EM algorithm is used to obtain the maximum likelihood...