Reliability Analysis via Non-Gaussian State-Space Models

This paper proposes new reliability models whose likelihood consists of decomposition of data information in stages or times, thus leading to latent state parameters. Alternative versions of some well-known models such as piecewise exponential, proportional hazards, and software reliability models are shown to be included in our unifying framework. In general, latent parameters of many reliability models are high dimensional, and their inference requires approximating methods such as Markov chain Monte Carlo (MCMC) or Laplace. Latent states in our models are related across stages through a non-Gaussian state-space framework. This feature makes the models mathematically tractable and allows for the exact computation of the marginal likelihood function, despite the non-Gaussianity of the state. Our non-Gaussian evolution models circumvent the need for approximations, which are required in similar likelihood-based approaches. In addition, they allow for reduction of the dimension of the problem. Real-life examples illustrate the approach and indicate advantages over other existing models.