Details of Grant 

EPSRC Reference:	GR/R92530/01
Title:	Adaptive replacement and maintenance strategies based on nonparametric predictive inference
Principal Investigator:	Coolen-Schrijner, Dr P
Other Investigators:	Coolen, Professor F
Researcher Co-Investigators:
Project Partners:
Department:	Mathematical Sciences
Organisation:	Durham, University of
Scheme:	Fast Stream
Starts:	01 September 2002	Ends:	31 August 2004	Value (£):	62,089
EPSRC Research Topic Classifications:	Mathematical Aspects of OR Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
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Summary on Grant Application Form
Nonparametric predictive inference (NPI) is a recently developed statistical approach using few structural assumptions in addition to data. Application of such inferential methods for lifetimes of units enables fully adaptive strategies for replacement and maintenance (RM). We will develop and analyse NPI-based strategies for a variety of RM situations, including age and block replacement, inspection models, and systems. This requires new theory on combining classical stochastic processes with NPI, and further development of NPI including simulation methods. The new adaptive RM strategies will be evaluated via comparisons with methods presented in the literature. Several RM situations will require use of NPI for right-censored data, which has recently been developed. Computational aspects of this work are challenging, in particular convolutions in which interval probabilities, as typically occur in NPI, are combined with classical probabilities from stochastic processes. This work follows recent initial work in this area, by the PI and Cl, and has been made possible by recent developments of NPI. Adaptive RM strategies have been suggested from Bayesian inferential perspective, but the ability of those methods to adapt to process data is limited due to assumed parametric probability distributions for lifetimes of units, whereas NPI-based adaptive RM strategies are nonparametric and therefore promise to adapt better to process data.
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