A regression shrinkage method tailored to qualitative regressors and clustered data
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Anke Neumann1, Josiane Holstein, Gilles Chatellier
1Assistance Publique-Hôpitaux de Paris (AP-HP), 3, Avenue Victoria, F-75184 Paris Cedex 04, France.
Abstract:
We propose a shrinkage method for estimation in linear regression models with qualitative regressors. Due to the nature of the shrinkage constraint, this method tends to give estimates that are exactly zero for some groups of coefficients belonging to the same regressor. The method hence results in concise models, since some of the regressors are entirely eliminated. In conjunction with this estimation method, a model with a fixed cluster effect turns out to be closely related to frailty models. We apply the method for modelling hospital readmissions.
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