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- Christiane Belitz, Andreas Brezger, +9 authors Leyre Est́ıbaliz
- 2009

Special thanks go to (in alphabetical order of first names): Achim Zeileis for advertising HCL colors; Dieter Gollnow for computing and providing the map of Munich (a really hard job); Leo Held for advertising the program; Ludwig Fahrmeir for his patience with finishing the program and for carefully reading and correcting the manual; Ngianga-Bakwin Kandala… (More)

- Felix Heinzl, Gerhard Tutz
- 2013

In linear mixed models, the assumption of normally distributed random effects is often inappropriate and unnecessarily restrictive. The proposed approximate Dirichlet process mixture assumes a hierarchical Gaussian mixture that is based on the truncated version of the stick breaking presentation of the Dirichlet process. In addition to the weakening of… (More)

Longitudinal data often require a combination of flexible trends and individual-specific random effects. In this paper, we propose a fully Bayesian approach based on Markov chain Monte Carlo simulation techniques that allows for the semiparametric specification of both the trend function and the random effects distribution. Bayesian penalized splines are… (More)

- Felix Heinzl, Gerhard Tutz
- Biometrical journal. Biometrische Zeitschrift
- 2014

A method is proposed that aims at identifying clusters of individuals that show similar patterns when observed repeatedly. We consider linear-mixed models that are widely used for the modeling of longitudinal data. In contrast to the classical assumption of a normal distribution for the random effects a finite mixture of normal distributions is assumed.… (More)

- Felix Heinzl, Gerhard Tutz
- Statistics and Computing
- 2016

- Felix Heinzl, Gerhard Tutz
- 2013

SUMMARY: We consider additive mixed models for longitudinal data with a nonlinear time trend. As random effects distribution an approximate Dirichlet process mixture is proposed that is based on the truncated version of the stick breaking presentation of the Dirichlet process and provides a Gaussian mixture with a data driven choice of the number of mixture… (More)

- Felix Heinzl, Gerhard Tutz
- 2011

SUMMARY: In linear mixed models the assumption of normally distributed random effects is often inappropriate and unnecessary restrictive. The proposed Dirichlet process mixture assumes a hierarchical Gaussian mixture. In addition to the weakening of distributions assumptions the specification allows to estimate clusters of observations with a similar random… (More)

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