Doubly stochastic models for replicated spatio-temporal point processes

03/21/2019
by   Daniel Gervini, et al.
0

This paper proposes a log-linear model for the latent intensity functions of a replicated spatio-temporal point process. By simultaneously fitting correlated spatial and temporal Karhunen-Loève expansions, the model produces spatial and temporal components that are usually easy to interpret and capture the most important modes of variation and spatio-temporal correlation of the process. The asymptotic distribution of the estimators is derived. The finite sample properties are studied by simulations. As an example of application, we analyze bike usage patterns on the Divvy bike sharing system of the city of Chicago.

READ FULL TEXT

page 27

page 28

research
01/06/2023

Spatio-temporal determinantal point processes

Determinantal point processes are models for regular spatial point patte...
research
08/25/2022

Infill asymptotics for logistic regression estimators for spatio-temporal point processes

This paper discusses infill asymptotics for logistic regression estimato...
research
12/20/2019

Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment

Correlation of interference affects spatio-temporal aspects of various w...
research
04/30/2022

Wrangling multivariate spatio-temporal data with the R package cubble

Multivariate spatio-temporal data refers to multiple measurements taken ...
research
06/30/2021

Spatial kriging for replicated temporal point processes

This paper presents a kriging method for spatial prediction of temporal ...
research
10/12/2011

Combining Spatial and Temporal Logics: Expressiveness vs. Complexity

In this paper, we construct and investigate a hierarchy of spatio-tempor...
research
01/02/2023

Mixed moving average field guided learning for spatio-temporal data

Influenced mixed moving average fields are a versatile modeling class fo...

Please sign up or login with your details

Forgot password? Click here to reset