TY - UNPB
T1 - Infill asymptotics for logistic regression estimators for spatio-temporal point processes
AU - van Lieshout, Marie-Colette
AU - Lu, Changqing
PY - 2022/8/25
Y1 - 2022/8/25
N2 - This paper discusses infill asymptotics for logistic regression estimators for spatio-temporal point processes whose intensity functions are of log-linear form. We establish strong consistency and asymptotic normality for the parameters of a Poisson point process model and demonstrate how these results can be extended to general point process models. Additionally, under proper conditions, we also extend our central limit theorem to other unbiased estimating equations that are based on the Campbell--Mecke theorem.
AB - This paper discusses infill asymptotics for logistic regression estimators for spatio-temporal point processes whose intensity functions are of log-linear form. We establish strong consistency and asymptotic normality for the parameters of a Poisson point process model and demonstrate how these results can be extended to general point process models. Additionally, under proper conditions, we also extend our central limit theorem to other unbiased estimating equations that are based on the Campbell--Mecke theorem.
KW - Campbell--Mecke theorem
KW - infill asymptotics
KW - logistic regression estimator
KW - spatio-temporal point process
KW - unbiased estimating equation
U2 - 10.48550/arXiv.2208.12080
DO - 10.48550/arXiv.2208.12080
M3 - Preprint
BT - Infill asymptotics for logistic regression estimators for spatio-temporal point processes
PB - ArXiv.org
ER -