Semiparametric Marginal Models for Binary Longitudinal Data

  •  Salehin Chowdhury    
  •  Sanjoy Sinha    


In this paper, we propose and explore a semiparametric approach to analyzing longitudinal binary data often observed in clinical studies. We applied second-order GEE approach to analyze longitudinal binary responses based on a partially linear single-index model. We use a local polynomial smoothing technique to estimate the single-index. We study the empirical properties of the proposed estimators using simulations. The empirical results demonstrate that if the true underlying model is partially linear, then our proposed method generally provides unbiased and efficient estimators. The proposed method is also applied to some real data sets obtained from longitudinal studies.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

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