Longitudinal Data

Longitudinal Data
Missing Data
Random Effects
Nonlinearity
Heterogeneity
Survival Analysis

Interest Keywords

MNAR Missing Data, Censored Data, Nonlinearity, survival analysis, Alzheimer’s Disease

Related Story

  • 2021: Undergraduate time-series coursework sparked my interest in time-dependent data, particularly in estimation under temporal dependence.
  • 2022: Dr. Colin Cameron mentored my first independent research on applying machine learning to time-series prediction. Beyond technical skills, his rigor and passion for research deeply shaped my academic mindset.
  • 2024-2025: Dr. Ani Eloyan introduced me to biostatistical research in Alzheimer’s disease and guided me into longitudinal data analysis. I am grateful for her trust and support in allowing me to lead two projects, through which I developed Bayesian integration methods for handling censored and missing data and further extended a Bayesian-based RNN to simultaneously address both challenges in EOAD longitudinal datasets.
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Citation

BibTeX citation:
@online{untitled,
  author = {},
  title = {Longitudinal {Data}},
  url = {https://kaizhongmu.github.io/research/longitudinal/},
  langid = {en}
}
For attribution, please cite this work as:
“Longitudinal Data.” n.d. https://kaizhongmu.github.io/research/longitudinal/.