Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …

ABSTRACT A comprehensive, unified approach to modeling arbitrarily censored spatial
survival data is presented for the three most commonly used semiparametric models …

Survival analysis has received a great deal of attention as a subfield of Bayesian
nonparametrics over the last 50 years. In particular, the fitting of survival models that allow …

Progressively more and more attention has been paid to how location affects health
outcomes. The area of disease mapping focusses on these problems, and the Bayesian …

Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most
important disease of cucurbits globally, and the pathogen is disseminated aerially over a …

The conditional autoregressive model and the intrinsic autoregressive model are widely
used as prior distribution for random spatial effects in Bayesian models. Several authors …

Survival methods are used for the statistical modelling of time-to-event data. Survival data
are characterized by a set of complete records, in which the time of the event is known; and …

Objectives. The main objective of this study was to establish the spatial variation in
ambulance response times for out-of-hospital cardiac arrests (OHCAs) in the city-state of …

Disease mapping is an important statistical tool used by epidemiologists to assess
geographic variation in disease rates and identify lurking environmental risk factors from …