Conditional Transformation Models - Interpretable Parametrisations and Censoring

@inproceedings{Mst2015ConditionalTM,
  title={Conditional Transformation Models - Interpretable Parametrisations and Censoring},
  author={Lisa M{\"o}st},
  year={2015}
}
Most well-known regression models focus on the estimation of the conditional mean given a set of explanatory variables. Higher moments of the distribution function are usually assumed as constant. This typically implies strict assumptions such as homoscedasticity or symmetry. In contrast, in the flexible model class of conditional transformation models (CTMs), the whole conditional distribution function is modelled directly. Thereby, higher moments of the conditional distribution (i.e. variance… CONTINUE READING

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