Space-Time Point Processes: An Applied Statistics Course (Springer Texts in Statistics)

The book focuses on applied methodology, and summarizes the main issues in the practical applications associated with space-time point processes.In particular, the questions addressed in this book are:How can one summarize space-time point process data?How are space-time point processes modeled?What are the different ways of estimating parameters in space-time point process models, and how do they compare?How can space-time point process models be estimated non-parametrically?What techniques exist for assessing how well a space-time point process model fits to data, or for comparing the fit of multiple models?How can one use a space-time point process model to forecast the probability of future events?Applied examples are used throughout the book, and the text includes R code for implementing all the techniques discussed in the book.The book covers standard, classical methods for point processes, such as Poisson processes, Cox processes, Neyman-Scott processes, Hawkes models, condition