Erfanul Hoque, PhD

Research

In an increasingly data-driven world, understanding and developing robust methodologies for complex and correlated data is crucial for advancing scientific and technological innovation across various fields, including health and environmental science. My research program revolves around complex and correlated data which presents unique statistical/biostatistical challenges and opportunities. My main research interest focuses on the methodological developments in statistical dependence modeling in multivariate data. Primarily, my interest is in heterogeneous dependence models with the aim to provide unified and flexible representations of complex dependencies in univariate and multivariate complex and correlated data (such as longitudinal data, time series data). Currently, my research focuses on developing and applying complex and correlated data models and data science methods to solve real-world problems in life, health and environmental sciences and advancing state-of-the-art statistical/biostatistical and epidemiological methods to generate reliable real-world evidence.

The main themes of my current research are:

• Incorporate heterogeneity in the dependence structure

  Modeling interdependencies and incorporating heterogeneity in the dependence structure among a large number of variables is a difficult task, partly because not many statistical models can accommodate flexibility in higher dimensions. Here, the goal is to develop novel models and methods that account for potential heterogeneity in the dependence structure of complex longitudinal data, with applications in medical studies (evolution of disease in subjects over time, identifying risk factors) and environmental studies (evaluating the long-term effects of environmental exposures on human and animal health).

• Models for random effects covariance structure

  In many applications, researchers are interested in modeling the dependence of the covariance of random effects in terms of covariates which is a longstanding open problem. The objective here is to develop models and inferential procedures for random effects covariance structure for longitudinal data with various complex features (missing reponses and/or covariates, measurement errors etc.) and functional longitudinal data as well as Bayesian inference for random effects models.

• Statistical machine learning for complex and correlated data
• Dynamic data science models and applications in health science