Showing papers by "Bo Henry Lindqvist published in 2003"

The trend-renewal process for statistical analysis of repairable systems

Abstract: The most commonly used models for the failure process of a repairable system are nonhomogeneous Poisson processes, corresponding to minimal repairs, and renewal processes, corresponding to perfect repairs. This article introduces and studies a more general model for recurrent events, the trend-renewal process (TRP). The TRP is a time-transformed renewal process having both the ordinary renewal process and the nonhomogeneous Poisson process as special cases. Parametric inference in the TRP model is studied, with emphasis on the case in which several systems are observed in the presence of a possible unobserved heterogeneity between systems.

Mathematical and statistical methods in reliability

Abstract: Reliability Theory in the Past and Present Centuries General Aspects of Reliability Modelling Reliability of Networks and Systems Stochastic Modelling and Optimization in Reliability Modelling in Survival and Reliability Analysis Statistical Methods for Degradation Data Statistical Methods for Maintained Systems Statistical Inference in Survival Analysis Software Reliability Methods.

A counterexample to a claim about stochastic simulations

Abstract: Engen & Lillegard (1997) presented a general method for doing Monte Carlo simulations conditioned on a sufficient statistic. The basic idea was to adjust the parameter values in the corresponding unconditional simulation so that the actual value of the sufficient statistic is obtained, and the claim was that if this adjustment is unique then the modified simulation is from the conditional distribution. Unfortunately the claim is not correct, as shown by a counterexample.

A class of tests for renewal process versus monotonic and nonmonotonic trend in repairable systems data

Abstract: A class of statistical tests for trend in repairable systems data based on the general null hypothesis of a renewal process is proposed. This class does in particular include a test which is attractive for general use by having good power properties against both monotonic and nonmonotonic trends. Both the single system and the several systems cases are considered.

Estimation and Inference in Nonparametric Cox-models: Time Transformation Methods

Abstract: In this paper generalization of the Cox proportional hazards regression model to a completely nonparametric model with an unspecified smooth covariate function is studied. A class of methods for Cox-regression called time transformation methods are defined, and a new method for nonparametric Cox-regression in this class is in particular studied. It turns out that this method enjoys a number of useful properties. Ways of doing inference and model checking in nonparametric Cox-models are also discussed, and a brief overview and comparison of methods for nonparametric Cox-regression is given.

Bounds for the Reliability of Multistate Systems with Partially Ordered State Spaces and Stochastically Monotone Markov Transitions

Abstract: Consider a multistate system with partially ordered state space E, which is divided into a set C of working states and a set D of failure states. Let X(t) be the state of the system at time t and suppose {X(t)} is a stochastically monotone Markov chain on E. Let T be the failure time, i.e., the hitting time of the set D. We derive upper and lower bounds for the reliability of the system, defined as Pm(T > t) where m is the state of perfect system performance.