An efficient method for the estimation of parameters of stochastic gamma process from noisy degradation measurements

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Volumn 227, Issue 4, 2013, Pages 425-433

  Lu, Dongliang ^a   Pandey, Mahesh D ^a   Xie, Wei Chau ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Gamma process; high dimension integration; likelihood analysis; quasi Monte Carlo; stochastic degradation model

Indexed keywords

GAMMA PROCESS; HIGH DIMENSIONS; LIKELIHOOD ANALYSIS; QUASI-MONTE CARLO; STOCHASTIC DEGRADATION;

ESTIMATION; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; POPULATION STATISTICS; RANDOM ERRORS; STOCHASTIC SYSTEMS;

PARAMETER ESTIMATION;

EID: 84884144934     PISSN: 1748006X     EISSN: 17480078     Source Type: Journal    
DOI: 10.1177/1748006X13477008     Document Type: Article

References (13)

1. Yuan XX, Pandey MD, Bickel GA. A probabilistic model of wall thinning in CANDU feeders due to flow-accelerated corrosion. Nuclear Engng Des. 2008 ; 238 (1). 16-24
2. Pandey MD, Lu D, Komljenovic D. The impact of probabilistic modeling in life-cycle management of nuclear piping systems. J Engng Gas Turb Power. 2011 ; 133 (1). 012901
3. Grewal MS, Andrews AP Kalman filtering: theory and practice using MATLAB. 3 rd ed.Hoboken, New Jersey: Wiley-IEEE Press, 2008. Hoboken, New Jersey: Wiley-IEEE Press ; 2008 :
4. Kallen MJ, van Noortwijk JM. Optimal maintenance decisions under imperfect inspection. Rel Engng Sys Saf. 2005 ; 90 (2-3). 177-185
5. Genz A. Numerical computation of multivariate normal probabilities. J Computat Graphical Stats. 1992 ; 1 (2). 141-149
6. Niederreiter H Random number generation and quasi-Monte Carlo methods. Philadelphia, Pennsylvania: Society for Industrial and Applied Mathematics ; 1992 :
7. Caflisch RE. Monte Carlo and quasi-Monte Carlo methods. Acta Numerica. 1998 ; 7: 1-49
8. Halton JH. Radical-inverse quasi-random point sequence. Commun ACM. 1964 ; 7 (12). 701-702
9. Sobol IM. On the distribution of points in a cube and the approximate evaluation of integrals. Zhurnal Vychislitelnńoi Matematiki i Matematicheskoi Fiziki. 1967 ; 7 (4). 784-802
10. Beckers M, Haegemans A Numerical integration: recent developments, software and applications. Espelid TO Genz A, ed. NATO ASI Series. Boston: Kluwer Academic Publishers ; 1992: 329-340.
11. Pawitan Y In all likelihood: statistical modelling and inference using likelihood. Oxford University Press ; 2001 :
12. Cheng T, Pandey MD. An accurate analysis of maintenance cost of structures experiencing stochastic degradation. Struct Infrastruct Engng. 2012 ; 8 (4). 329-339
13. Pandey MD, Cheng TJ, van der Weide JAM. Finite time maintenance cost analysis of engineering systems affected by stochastic degradation. Proc IMechE, Part O: J Risk and Reliability. 2011 ; 225 (2). 241-250