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"Reliability Inference Based on Multistate and Degradation Models"

Summary: "Reliability Inference Based on
Multistate and Degradation Models"
Vijay Nair
Department of Statistics
Department of Industrial & Operations Engineering
University of Michigan, Ann Arbor
The University of Georgia
Department of Statistics
Colloquium Series
Reliability or survival analysis is traditionally based on time-to-failure data. In high-
reliability applications, there is usually a high degree of censoring, which causes difficul-
ties in making reasonable inference. There are a number of alternatives to increasing the
efficiency of reliability inference in such cases: accelerated testing, collection and use of
extensive covariate information, and the use of multistate and degradation data when
available. This talk will focus on the last topic. The first part of the talk deals with degrada-
tion data. We will review some common models for analyzing degradation data and then
describe a class of models based on non-homogeneous Gaussian processes. Properties of
the models and methods for inference will be discussed. We will then describe different
multistate models that arise in applications and discuss inference for semi-Markov multi-
state models with panel data (interval censoring), a common type of data collection


Source: Arnold, Jonathan - Nanoscale Science and Engineering Center & Department of Genetics, University of Georgia


Collections: Biotechnology