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Showing 1 to 5 of 5 for “"Penalized estimation"”.

  1. A penalized estimation procedure for varying coefficient models

    … is a recurrent topic in nonparametric function estimation. A function has global sparsity if it is zero over the entire domain, and it indicates that the corresponding covariate is irrelevant to the response variable. A function has local sparsity if it is nonzero but remains zero for a set of …

    colostate Repository record for A penalized estimation procedure for varying coefficient models (opens in a new tab)

  2. Nonlinear penalized estimation of true Q-matrix in cognitive diagnostic models

    A key issue of cognitive diagnostic models (CDMs) is the correct identification of Q-matrix which indicates the relationship between attributes and test items. Previous CDMs typically assumed a known Q-matrix provided by domain experts such as those who developed the questions. However, …

    columbia-diss Repository record for Nonlinear penalized estimation of true Q-matrix in cognitive diagnostic models (opens in a new tab)

  3. Penalized Joint Maximum Likelihood Estimation Applied to Two Parameter Logistic Item Response Models

    … will yield meaningless results. Recently, penalized estimation methods have been developed to analyze data sets that may include more variables than observations. The main focus of this study was to apply LASSO and ridge regression penalization techniques to IRT models in order to better …

    columbia-diss Repository record for Penalized Joint Maximum Likelihood Estimation Applied to Two Parameter Logistic Item Response Models (opens in a new tab)

  4. Contributions to statistical learning and its applications in personalized medicine

    … in the study of regularization methods based on penalized estimation. Those procedures find an estimator that is the result of an optimization problem balancing out the fitting to the data with the plausability of the estimation. The first chapter studies a smoothness regularization estimator for …

    gatech Repository record for Contributions to statistical learning and its applications in personalized medicine (opens in a new tab)