@TechReport{dp-496,
  author        = {Sibbertsen, Philipp and Willert, Juliane},
  astring       = {Philipp Sibbertsen and Juliane Willert},
  title         = {Estimating the number of mean shifts under long memory},
  month         = {March},
  year          = {2012},
  pages         = {16},
  size          = {144},
  number        = {496},
  language      = {en},
  keywords      = {long memory, mean shift, regression tree, ART, LWZ, LIC.},
  jelclass      = {C14, C22},
  abstract      = {Detecting the number of breaks in the mean can be
                  challenging when it comes to the long memory framework.
                  Tree-based procedures can be applied to time series when
                  the location and number of mean shifts are unknown and
                  estimate the breaks consistently though with possible
                  overfitting. For pruning the redundant breaks information
                  criteria can be used. An alteration of the BIC, the LWZ, is
                  presented to overcome long-range dependence issues. A Monte
                  Carlo Study shows the superior performance of the LWZ to
                  alternative pruning criteria like the BIC or LIC.}
}