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We study regularized estimation in high‐dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent...
Item response theory (IRT) is a family of statistical psychometric models for discretely scored responses of subjects (students, survey respondents, etc.) to items (questions) on exams, surveys, and so on, using a continuous latent variable to represent the general propensity of each subject to respond positively to each item or question.
We give a historical introduction to item response theory, which places the work of Thurstone, Lord, Guttman and Coombs in a present-day perspective. The general assumptions of modern item response theory, local independence and monotonicity of response functions, are discussed, followed by a general framework for estimating item response models. Six classes of well-known item response models and...
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