Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
We propose a general framework of using a multi-level log-Gaussian Cox process to model repeatedly observed point processes with complex structures; such type of data have become increasingly available in various areas including medical research, social sciences, economics, and finance due to technological advances. A novel nonparametric approach is developed to efficiently and consistently estimate the covariance functions of the latent Gaussian processes at all levels. To predict the functional principal component scores, we propose a consistent estimation procedure by maximizing the conditional likelihood of super-positions of point processes. We further extend our procedure to the bivariate point process casemore »
-
Free, publicly-accessible full text available June 1, 2023
-
Free, publicly-accessible full text available March 1, 2023
-
Free, publicly-accessible full text available December 1, 2022
-
Free, publicly-accessible full text available November 1, 2022
-
Free, publicly-accessible full text available September 1, 2022
-
null (Ed.)Free, publicly-accessible full text available July 1, 2022
-
Free, publicly-accessible full text available April 1, 2023