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Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data
In the quest to improve services, companies offer customers the opportunity to interact with agents through contact centers, where the communication is mainly text-based.
Kernel Machines With Missing Responses
For the quadratic loss, we then propose a family of doubly-robust kernel machines.
Kernel Machines for Current Status Data
In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring.
Feature Elimination in Kernel Machines in moderately high dimensions
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features. We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present four case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.
