Variate Generation in Reliability

This chapter considers (1) the generation of random lifetimes via density-based and hazard-based methods, (2) the generation of certain stochastic processes that are useful in reliability and availability analysis, and (3) the generation of random lifetimes for the accelerated life and proportional hazards models. The accurate modeling of failure time distributions is critical for the development of valid Monte Carlo and discrete-event simulation models for applications in reliability and survival analysis. Once an accurate model has been established, it is oftentimes the case that the complexity of the model requires an analysis by simulation. The associated variate generation algorithms for common stochastic models are introduced here. Although the generation of random lifetimes is typically applied to reliability and survival analysis in a simulation setting, their use is widespread in other disciplines as well. The more diverse wider literature on generating random objects includes generating random combinatorial objects, generating random matrices, generating random polynomials, generating random colors, generating random geometric objects, and generating random spawning trees.

[1]  Shane G. Henderson,et al.  Behavior of the NORTA method for correlated random vector generation as the dimension increases , 2003, TOMC.

[2]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[3]  Erhan Çinlar,et al.  Introduction to stochastic processes , 1974 .

[4]  W. Nelson Statistical Methods for Reliability Data , 1998 .

[5]  John O'Quigley,et al.  Proportional Hazards Regression , 2008 .

[6]  J. Lawless Statistical Models and Methods for Lifetime Data , 2002 .

[7]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[8]  M. Carmeli Statistical Theory And Random Matrices , 1983 .

[9]  I. Olkin,et al.  Generating Correlation Matrices , 1984 .

[10]  Melba M. Crawford,et al.  Modeling and simulation of a nonhomogeneous poisson process having cyclic behavior , 1991 .

[11]  Ralf Bender,et al.  Generating survival times to simulate Cox proportional hazards models , 2005, Statistics in medicine.

[12]  Brent Morris Magic Tricks, Card Shuffling and Dynamic Computer Memories , 1998 .

[13]  P. Deift Orthogonal Polynomials and Random Matrices: A Riemann-Hilbert Approach , 2000 .

[14]  Paul D. Allison,et al.  Event History Analysis : Regression for Longitudinal Event Data , 1984 .

[15]  R Disney,et al.  Probability and random processes : a first course with applications , 1985 .

[16]  Donald E. Knuth,et al.  The art of computer programming. Vol.2: Seminumerical algorithms , 1981 .

[17]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[18]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[19]  H. A. David The theory of competing risks , 1980 .

[20]  Frederic Paik Schoenberg,et al.  Multidimensional Residual Analysis of Point Process Models for Earthquake Occurrences , 2003 .

[21]  P. Allison Event History Analysis , 1984 .

[22]  G. Shedler,et al.  Simulation of Nonhomogeneous Poisson Processes by Thinning , 1979 .

[23]  Asit P. Basu,et al.  Statistical Methods for the Reliability of Repairable Systems , 2000 .

[24]  Albert Nijenhuis,et al.  Combinatorial Algorithms for Computers and Calculators , 1978 .

[25]  Herbert S. Wilf,et al.  Combinatorial Algorithms: An Update , 1987 .

[26]  Sheldon M. Ross Introduction to Probability Models. , 1995 .

[27]  S. Resnick Adventures in stochastic processes , 1992 .

[28]  Ward Whitt,et al.  An Introduction to Stochastic-Process Limits and their Application to Queues , 2002 .

[29]  Lawrence Leemis Technical Note - Variate Generation for Accelerated Life and Proportional Hazards Models , 1987, Oper. Res..

[30]  H. A. David,et al.  The Theory of Competing Risks. , 1979 .

[31]  D. Cox,et al.  Analysis of Survival Data. , 1985 .

[32]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[33]  Barry L. Nelson,et al.  Input modeling when simple models fail , 1995, WSC '95.

[34]  Wayne B. Nelson,et al.  Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications , 2002 .

[35]  Ralf Bender,et al.  Generating survival times to simulate Cox proportional hazards models by Ralf Bender, Thomas Augustin and Maria Blettner, Statistics in Medicine 2005; 24:1713–1723 , 2006, Statistics in medicine.

[36]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[37]  B. Everitt,et al.  Finite Mixture Distributions , 1981 .

[38]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[39]  L. Devroye Non-Uniform Random Variate Generation , 1986 .

[40]  Alan Edelman,et al.  How many zeros of a random polynomial are real , 1995 .

[41]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[42]  M. Crowder Classical Competing Risks , 2001 .

[43]  K. Preston White Simulating a nonstationary Poisson process using bivariate thinning: the case of “typical weekday” arrivals at a consumer electronics store , 1999, WSC '99.

[44]  Wolfgang Hörmann,et al.  Automatic Nonuniform Random Variate Generation , 2011 .

[45]  A. Edelman,et al.  How many eigenvalues of a random matrix are real , 1994 .

[46]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .