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DTSTAMP:20250505T133834Z
DESCRIPTION:Lu Xia\, PhD\n\nAssistant Professor\n	Department of Statistics a
 nd Probability |\n	Michigan State University\n\nWHEN: Wednesday\, Sept 4\, 
 2024\, from 3:30 to 4:30 p.m.\n	WHERE: Virtual | Zoom\n	NOTE: Lu Xia will be
  presenting from Michigan\n\nAbstract\n\nRegression analysis of longitudin
 al data\, where correlated responses from multiple time points are measure
 d\, is ubiquitous in many scientific areas such as biology\, medicine and 
 sociology. New challenges are emerging with the availability of complex lo
 ngitudinal data in this big data era. Generalized estimating equations (GE
 E) are widely used to analyze longitudinal data\, which can achieve consis
 tency and improve efficiency. The first part of this talk will introduce a
  projected estimating equation approach to reliably drawing inference for 
 linear functionals of regression parameters in GEE in high-dimensional set
 tings\, where the number of covariates (such as proteomic features) may ex
 ceed the sample size. The second part of this talk will focus on a transfe
 r learning approach for GEE that facilitates parameter estimation and pred
 iction in the target population when external models from larger cohorts a
 re readily available.\n\nSpeaker Bio\n\nLu Xia is an Assistant Professor i
 n the Department of Statistics and Probability at Michigan State Universit
 y. Before joining Michigan State in 2023\, she earned her PhD in Biostatis
 tics from the University of Michigan and worked as a Postdoctoral Scholar 
 in the Department of Biostatistics at the University of Washington after g
 raduation. Her current research focuses on high-dimensional statistics\, d
 ata integration\, machine learning and multi-omics data analysis. For more
  information\, please visit https://sites.google.com/view/luxia.\n\n \n\n 
 \n
DTSTART:20240904T193000Z
DTEND:20240904T203000Z
LOCATION:Online
SUMMARY:Estimating Equation-Based Approaches for Complex Longitudinal Data:
  High-Dimensional Inference and Transfer Learning
URL:/epi-biostat-occh/channels/event/estimating-equati
 on-based-approaches-complex-longitudinal-data-high-dimensional-inference-a
 nd-358795
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