Abstract
A major issue in exploring and analyzing complex life history data with multiple states and recurrent events is the development and availability of flexible models and methods that allow to discover unknown dynamics of underlying transition intensities, to model and to estimate nonlinear functional forms of covariates and time-varying effects, to include time-dependent covariates, and to deal with multivariate time scales. In this paper we propose and develop a nonparametric multiplicative hazard model that takes into account these aspects. Embedded in the counting process framework, estimation is based on penalized likelihoods and splines. We illustrate our approach by an application to sleep-electroencephalography data with multiple recurrent states of human sleep.
Dokumententyp: | Paper |
---|---|
Fakultät: | Mathematik, Informatik und Statistik > Statistik > Sonderforschungsbereich 386
Sonderforschungsbereiche > Sonderforschungsbereich 386 |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 510 Mathematik |
Sprache: | Englisch |
Dokumenten ID: | 1479 |
Datum der Veröffentlichung auf Open Access LMU: | 04. Apr. 2007 |
Letzte Änderungen: | 11. Mai 2017, 14:04 |