seminars:stat:180301 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>

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^  **DATE:**|Thursday, March 1, 2018 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Ganggang Xu, Binghamton University |
^  **TITLE:**|Structured Multi-Level Log-Gaussian Cox Processes |
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<WRAP centeralign>**Abstract**</WRAP>
 We propose a general framework of using multi-level log-Gaussian Cox
processes to model repeatedly observed point processes with complex structures.
A novel nonparametric approach is developed to consistently estimate the covariance kernels of the latent Gaussian processes at all levels. Consequently, multi-level
functional  principal  component  analysis  can  be  conducted  to  investigate  the  various sources of variations in the observed point patterns.  We further extend our procedure to the bivariate point process case in which potential correlations between the processes can be assessed.  
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