@phdthesis{Kann2020,
  author    = {Kann, Lennart},
  title     = {Statistical Failure Prediction with an Account for Prior Information},
  doi       = {10.25972/OPUS-20504},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205049},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Prediction intervals are needed in many industrial applications. Frequently in mass production, small subgroups of unknown size with a lifetime behavior differing from the remainder of the population exist. A risk assessment for such a subgroup consists of two steps: i) the estimation of the subgroup size, and ii) the estimation of the lifetime behavior of this subgroup. This thesis covers both steps. An efficient practical method to estimate the size of a subgroup is presented and benchmarked against other methods. A prediction interval procedure which includes prior information in form of a Beta distribution is provided. This scheme is applied to the prediction of binomial and negative binomial counts. The effect of the population size on the prediction of the future number of failures is considered for a Weibull lifetime distribution, whose parameters are estimated from censored field data. Methods to obtain a prediction interval for the future number of failures with unknown sample size are presented. In many applications, failures are reported with a delay. The effects of such a reporting delay on the coverage properties of prediction intervals for the future number of failures are studied. The total failure probability of the two steps can be decomposed as a product probability. One-sided confidence intervals for such a product probability are presented.},
  subject      = {Konfidenzintervall},
  language  = {en}
}
@article{RodriguezEntrenaSchuberthGelhard2018,
  author    = {Rodr{\´i}guez-Entrena, Macario and Schuberth, Florian and Gelhard, Carsten},
  title     = {Assessing statistical differences between parameters estimates in Partial Least Squares path modeling},
  series = {Quality \& Quantity},
  volume    = {52},
  journal   = {Quality \& Quantity},
  number    = {1},
  doi       = {10.1007/s11135-016-0400-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226403},
  pages     = {57-69},
  year      = {2018},
  abstract  = {Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.},
  language  = {en}
}