A New Efficient Algorithm for Construction of GOM Models

Mikhail Kovtun, Duke University
Igor Akushevich, Duke University
Kenneth G. Manton, Duke University
H. Dennis Tolley, Brigham Young University

We present a new efficient algortithm for construction of grade of membership (GoM) models. This algorithm reduces a problem of estimation of model parameters to a sequence of problems of linear algebra, which assures a low computational complexity and ability to handle on desktop computers data that involve up to thousands of variables.

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Presented in Poster Session 6: Applied Demography, Methods, Health and Mortality