Monotonic regression based on Bayesian P-splines: An application to estimating price response functions from store-level scanner data
Monotonic regression based on Bayesian P-splines: An application to estimating price response functions from store-level scanner data
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DOI：
10.1198/073500107000000223
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发表时间：
2008-01-01
影响因子：
3
通讯作者：
Steiner, Winfried J.
中科院分区：
文献类型：
--
作者：
Brezger, Andreas;Steiner, Winfried J.
在许多实际情况下，需要限制非参数估计的灵活性，以适应协变量和响应变量之间假定的单调关系。我们遵循贝叶斯方法，使用惩罚B-样条，并将单调性的假设在一个自然的方式通过适当的规范各自的先验分布。我们说明的方法，在实证应用建模需求品牌的橙子汁，并表明，施加单调性约束，为自己和跨项目的价格影响，大大提高了预测的有效性估计的销售响应函数。
In many practical situations, it is desirable to restrict the flexibility of nonparametric estimation to accommodate a presumed monotonic relationship between a covariate and the response variable. We follow a Bayesian approach using penalized B-splines and incorporate the assumption of monotonicity in a natural way by an appropriate specification of the respective prior distributions. We illustrate the methodology in an empirical application modeling demand for brands of orange juice and show that imposing monotonicity constraints for own- and cross-item price effects improves the predictive validity of the estimated sales response functions considerably.