Olanrewaju Akande
Student

I am a fourth-year PhD candidate working on developing statistical methodology for handling missing and faulty data, with particular emphasis on applications that intersect with the social sciences. I am especially motivated to develop methods that can be readily applied by statistical agencies and data analysts. My work focuses on creating a coherent imputation engine that can handle missing data and reporting error, leverage auxiliary information on marginal distributions, incorporate survey weights, and scale up to a large number of categorical variables.

Current Research Interests

Bayesian modeling, models for editing erroneous data, multiple imputa- tion, missing data, mixture models, and hierarchical modeling.

Current Appointments & Affiliations

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