Mixed Membership Trajectory Models
نویسنده
چکیده
9.
منابع مشابه
Longitudinal Mixed Membership Trajectory Models for Disability Survey Data.
We develop new methods for analyzing discrete multivariate longitudinal data and apply them to functional disability data on U.S. elderly population from the National Long Term Care Survey (NLTCS), 1982-2004. Our models build on a mixed membership framework, in which individuals are allowed multiple membership on a set of extreme profiles characterized by time-dependent trajectories of progress...
متن کاملIntroduction to Mixed Membership Models and Methods
1.1 Historical Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 A General Formulation for Mixed Membership Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Advantages of Mixed Membership Models in Applied Statistics . . . . . . . . . . . . . . . . . . ....
متن کاملCharacterizing Long-Term Patterns of Weight Change in China Using Latent Class Trajectory Modeling
BACKGROUND Over the past three decades, obesity-related diseases have increased tremendously in China, and are now the leading causes of morbidity and mortality. Patterns of weight change can be used to predict risk of obesity-related diseases, increase understanding of etiology of disease risk, identify groups at particularly high risk, and shape prevention strategies. METHODS Latent class t...
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Mixed-membership network models permit a node in a graph to take on different latent roles in different interactions. However, while mixed-membership block models often do out-perform classical network models, the actual degree of mixed-membership in many graphs is small, with nodes usually taking on only a handful of many possible roles. We thus present a novel slightly mixed membership stocha...
متن کاملMixed Membership Models for Time Series
20.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 20.1.1 State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 20.1.2 Latent Dirichlet Allocation . . . . . . . . . . . ...
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