Penalized models to estimate customer survival |
| |
Authors: | Silvia Figini |
| |
Institution: | (1) Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand |
| |
Abstract: | In this paper we propose a novel procedure, for the estimation of semiparametric survival functions. The proposed technique
adapts penalized likelihood survival models to the context of lifetime value modeling. The method extends classical Cox model
by introducing a smoothing parameter that can be estimated by means of penalized maximum likelihood procedures. Markov Chain
Monte Carlo methods are employed to effectively estimate such smoothing parameter, using an algorithm which combines Metropolis–Hastings
and Gibbs sampling. Our proposal is contextualized and compared with conventional models, with reference to a marketing application
that involves the prediction of customer’s lifetime value estimation. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|