@article{PohleHosoyaPohleetal.2022,
  author    = {Pohle, Lara and Hosoya, Georg and Pohle, Jennifer and Meyer-Jenßen, Lars},
  title     = {The relationship between early childhood teachers' instructional quality and children's mathematics development},
  series = {Learning and instruction : the journal of the European Association for Research on Learning and Instruction},
  volume    = {82},
  journal   = {Learning and instruction : the journal of the European Association for Research on Learning and Instruction},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0959-4752},
  doi       = {10.1016/j.learninstruc.2022.101636},
  pages     = {12},
  year      = {2022},
  abstract  = {This study examined how early childhood (EC) teachers' instructional quality predicted children's development in mathematics across two measurement occasions. Therefore, EC teachers' (n = 25) instructional quality was assessed using one standardized observation instrument covering both domain-specific and general aspects of instructional quality. Additionally, data on children's (n = 208) outcome in early number skills was collected applying a standardized test. Multilevel structural equation modeling was used accounting for nested data. Children's age and the average size of preschool groups were controlled for. Results revealed that EC teachers' instructional quality predicted children's development but was not associated with their initial achievement. The findings suggest that instruments covering domain-specific and general aspects might be helpful in order to measure EC teachers' instructional quality in mathematics and predict children's learning growth. Understanding the mechanisms between instructional quality and children's development may help EC teachers to enhance their math teaching in practice.},
  language  = {en}
}
@article{PohleAdamBeumer2022,
  author    = {Pohle, Jennifer and Adam, Timo and Beumer, Larissa},
  title     = {Flexible estimation of the state dwell-time distribution in hidden semi-Markov models},
  series = {Computational statistics \& data analysis},
  volume    = {172},
  journal   = {Computational statistics \& data analysis},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-9473},
  doi       = {10.1016/j.csda.2022.107479},
  pages     = {15},
  year      = {2022},
  abstract  = {Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN.},
  language  = {en}
}