Local parametric sensitivity for mixture models of lifetime distributions

Mixture models are receiving considerable significance in the last years. Practical situations in reliability and survival analysis may be addressed by using mixture models. When making inferences on them, besides the estimates of the parameters, a sensitivity analysis is necessary. In this paper, a general technique to estimate local prior sensitivities in finite mixtures of distributions from natural exponential families having quadratic variance function (NEF-QVF) is proposed. Those families include some distributions of wide use in reliability theory. An advantage of this method is that it allows a direct implementation of the sensitivity measure estimates and their errors. In addition, the samples that are drawn to estimate the parameters in the mixture model are re-used to estimate the sensitivity measures and their errors. An illustrative application based on insulating fluid failure data is shown.

[1]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[2]  C. Holmes,et al.  MCMC and the Label Switching Problem in Bayesian Mixture Modelling 1 Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modelling , 2004 .

[3]  Andrew L. Rukhin,et al.  Tools for statistical inference , 1991 .

[4]  Wilfried Seidel,et al.  Editorial: recent developments in mixture models , 2003, Comput. Stat. Data Anal..

[5]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[6]  Carlos J. Perez,et al.  Sensitivity estimations for Bayesian inference models solved by MCMC methods , 2006, Reliab. Eng. Syst. Saf..

[7]  C. Morris Natural Exponential Families with Quadratic Variance Functions: Statistical Theory , 1983 .

[8]  P. Green,et al.  Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .

[9]  Ashis SenGupta,et al.  Optimal Tests for No Contamination in Reliability Models , 2000, Lifetime data analysis.

[10]  K. Cowles,et al.  CODA: convergence diagnosis and output analysis software for Gibbs sampling output , 1995 .

[11]  J. F. C. Kingman,et al.  Information and Exponential Families in Statistical Theory , 1980 .

[12]  C. Morris,et al.  Exponential and bayesian conjugate families: Review and extensions , 1997 .

[13]  P. Deb Finite Mixture Models , 2008 .

[14]  Christian P. Robert,et al.  The Bayesian choice , 1994 .

[15]  M. Stephens Dealing with label switching in mixture models , 2000 .

[16]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[17]  C. Morris Natural Exponential Families with Quadratic Variance Functions , 1982 .

[18]  C. Robert,et al.  Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .

[19]  Ajay Jasra,et al.  Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling , 2005 .

[20]  Guido Consonni,et al.  Conjugate Priors for Exponential Families Having Quadratic Variance Functions , 1992 .

[21]  M. J. Rufo,et al.  Bayesian analysis of finite mixture models of distributions from exponential families , 2006, Comput. Stat..

[22]  Bruce G. Lindsay,et al.  A review of semiparametric mixture models , 1995 .

[23]  Jerry M. Davis,et al.  Bayesian analysis of the mixing function in a mixture of two exponential distributions , 2001 .

[24]  Marco Alfò,et al.  Advances in Mixture Models , 2007, Comput. Stat. Data Anal..