Titlebook: Complex Data Modeling and Computationally Intensive Statistical Methods; Pietro Mantovan,Piercesare Secchi Book 2010 Springer-Verlag Milan

amygdala

DNR215

鴿子

RAG

Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics,d by the non-parametric model, and is inferred from the data. Compared to previous results, we obtain narrower interval estimates of the quantiles of the predictive survival function. Other diagnostic plots, such as predictive tail probabilities and Bayesian residuals, show a good agreement between the model and the data.

SEMI

Predictive densities and prediction limits based on predictive likelihoods,ome specific applications, these solution usually improve on those ones based on the plug-in procedure. However, the associated predictive densities and prediction limits do not correspond to the optimal frequentist solutions already described in the literature.

Expediency

Vsd168

Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics,spool, subject to different stress levels. We propose a semi-parametric modelling by letting the error distribution be a shape-scale mixture of Weibull densities, the mixing measure being a normalised generalised gamma measure. We obtain posterior estimates of the regression parameter and also of cr

angiography

Callus

Exploitation, integration and statistical analysis of the Public Health Database and STEMI Archive Acute Myocardial Infarction”. The main goal of the Programme is the construction and statistical analysis of data coming from the integration of complex clinical and administrative databases concerning patients with Acute Coronary Syndromes treated in the Lombardia region. Clinical data sets arise f

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