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	Bivariate Geometric Distribution and Competing Risks: Statistical Analysis and Application Volf, Petr The contribution studies the statistical model for discrete time two-variate duration (time-to-event) data. The analysis is complicated by partial data observation caused either by the right-side censoring or by the presence of dependent competing events. The case is modeled and analyzed with the aid of a two-variate geometric distribution. The model identifiability is discussed and it is shown that the model is not identifiable without proper additional assumptions. The method of analysis is illustrated both on artificially generated\nexample and on real unemployment data. Detailed record
	Application of the Cox regression model with time dependent parameters to unemployment data Volf, Petr The contribution deals with the application of statistical survival analysis with the intensity described by a generalized version of Cox regression model with time dependent parameters. A\nmethod of model components non-parametric estimation is recalled, the flexibility of result is assessed with a goodness-of-fit test based on martingale residuals. The application\nconcerns to the real data representing the job opportunities development and reduction, during a given period. The risk of leaving the company is changing in time and depends also on the age of employees and their time with company. Both these covariates are considered and their impact to the risk analyzed. Detailed record
	Bayesovská analýza časových řad s kovariátami Volf, Petr Bayes methods (supported by MCMC computations) allows to deal with enhanced statistical models. It concerns also time series analysis, where the autoregressive character can be incorporated already to Bayes prior model and one can consider simultaneously a similar time development of other parameters. In present contribution the methodology is used to the analysis of time series of aggregated unemployment data, model contains regression on age, gender, region, and time-dependent variance. Detailed record

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