Abstract Number:
1850
Submission Type:
Topic-Contributed Paper Session
Participants:
Maryclare Griffin (1), Maryclare Griffin (1), Marie-Christine Duker (2), Ori Rosen (3), Sepideh Mosaferi (1), Scott Bruce (1), Toryn Schafer (4)
Institutions:
(1) N/A, N/A, (2) Cornell University, N/A, (3) Univ of Texas at El Paso, N/A, (4) Texas A&M University, N/A
Chair:
Session Organizer:
Speaker(s):
Session Description:
This session is focused on introducing several novel statistical methods for time series data. Specifically, the content of this session will be five presentations selected to highlight new models that (i) employ spectral methods to efficiently analyze high dimensional time series, (ii) incorporate modern learning methods including neural networks and trees, and (iii) build on shrinkage prior based regularization methods. This session will appeal to statistical methodologists with a range of backgrounds, both within and outside of the time series community. The session will also appeal to more applied statisticians who are interested in learning about new time series models that may address challenges they encounter when analyzing data in practice.
- Marie-Christine Düker, marie.dueker@fau.de, FAU Erlangen, "Subordinated functional time series with applications to neural networks"
- Ori Rosen, orosen@utep.edu, University of Texas El Paso, "Spatially Adaptive Spectral Estimation"
- Sepideh Mosaferi, smosaferi@umass.edu, University of Massachusetts Amherst, "Properties of Test Statistics for Nonparametric Cointegrating Regression Functions Based on Subsamples"
- Scott Bruce, sabruce@tamu.edu, Texas A&M University, "Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis"
- Toryn Schafer, tschafer@tamu.edu, Texas A&M University, "Locally adaptive shrinkage priors for trends and breaks in complex time series"
The format of this session will be a chaired session with five speakers.
Sponsors:
Business and Economic Statistics Section 1
Section on Statistical Computing 2
Section on Statistical Learning and Data Science 3
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
No
Applied
No
Estimated Audience Size
Medium (80-150)
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
I understand