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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Space-Time Point Processes: An Applied Statistics Course

Space-Time Point Processes: An Applied Statistics Course

Schoenberg, Frederic

Hardcover

Series: Springer Texts in Statistics

Probability & Statistics

Currently unavailable to order

ISBN10: 3032207894
ISBN13: 9783032207890
Publisher: Springer
Published: Jun 16 2026
Pages: 133
Weight: 1.01
Height: 0.56 Width: 7.32 Depth: 10.11
Language: English
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, conditional intensities, kernel smoothing, and Ripley's K-function, and also describes important recent advances for space-time point processes, such as Model-Independent Stochastic Declustering (MISD), Stoyan-Grabarnik parameter estimation, Voronoi deviance residuals, and super-thinned residuals.

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