Monotonic regression based on Bayesian P-splines: an application to estimating price response functions from store-level scanner data
提出一种贝叶斯P样条方法，在非参数回归中施加单调性约束，用于从门店扫描数据估计价格与销量的递减关系，并以橙汁品牌为例验证了该方法能显著提升预测准确性。
Generalized additive models have become a widely used instrument for flexible regression analysis. In many practical situations, however, it is desirable to restrict the flexibility of nonparametric estimation in order to accommodate a presumed monotonic relationship between a covariate and the response variable. For example, consumers usually will buy less of a brand if its price increases, and therefore one expects a brand's unit sales to be a decreasing function in own price. 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 a brand 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 function considerably.