Statistical Models Commons^™

Estimation And Variable Selection In High-Dimensional Settings With Mismeasured Observations, Michael Byrd

Statistical Science Theses and Dissertations

Understanding high-dimensional data has become essential for practitioners across many disciplines. The general increase in ability to collect large amounts of data has prompted statistical methods to adapt for the rising number of possible relationships to be uncovered. The key to this adaptation has been the notion of sparse models, or, rather, models where most relationships between variables are assumed to be negligible at best. Driving these sparse models have been constraints on the solution set, yielding regularization penalties imposed on the optimization procedure. While these penalties have found great success, they are typically formulated with strong assumptions on the …