@article{725001,
      author = {Kaeding, Matthias,},
      url = {http://library.usi.edu/record/725001},
      title = {Bayesian analysis of failure time data using P-Splines  [electronic resource] /},
      abstract = {Matthias Kaeding discusses Bayesian methods for analyzing  discrete and continuous failure times where the effect of  time and/or covariates is modeled via P-splines and  additional basic function expansions, allowing the  replacement of linear effects by more general functions.  The MCMC methodology for these models is presented in a  unified framework and applied on data sets. Among others,  existing algorithms for the grouped Cox and the piecewise  exponential model under interval censoring are combined  with a data augmentation step for the applications. The  author shows that the resulting Gibbs sampler works well  for the grouped Cox and is merely adequate for the  piecewise exponential model. Contents Relative Risk and  Log-Location-Scale Family Bayesian P-Splines Discrete Time  Models Continuous Time Models Target Groups Researchers and  students in the fields of statistics, engineering, and life  sciences Practitioners in the fields of reliability  engineering and data analysis involved with lifetimes The  Author Matthias Kaeding obtained his Master of Science  degree at the University of Bamberg in Survey Statistics.},
      doi = {https://doi.org/10.1007/978-3-658-08393-9},
      recid = {725001},
      pages = {1 online resource (ix, 110 pages) :},
}