Bayesian Semiparametric Modelling of Contraceptive Behaviour in India Via Sequential Logistic Regressions
针对印度不同时期家庭计划政策（如避孕目标、强制绝育等）的评估需求，提出一种贝叶斯半参数模型，通过条件伯努利选择重新参数化多项概率，以全面分析避孕方法选择的影响因素。
Summary Family planning has been characterized by highly different strategic programmes in India, including method-specific contraceptive targets, coercive sterilization and more recent target-free approaches. These major changes in family planning policies over time have motivated considerable interest towards assessing the effectiveness of the different planning programmes. Current studies mainly focus on the factors driving the choice among specific subsets of contraceptives, such as a preference for alternative methods other than sterilization. Although this restricted focus produces key insights, it fails to provide a global overview of the different policies, and of the determinants underlying the choices from the entire range of contraceptive methods. Motivated by this consideration, we propose a Bayesian semiparametric model relying on a reparameterization of the multinomial probability mass function via a set of conditional Bernoulli choices. This binary decision tree is defined to be consistent with the current family planning policies in India, and coherent with a reasonable process characterizing the choice between increasingly nested subsets of contraceptive methods. The model allows a subset of covariates to enter the predictor via Bayesian penalized splines and exploits mixture models to represent uncertainty in the distribution of the state-specific random effects flexibly. This combination of flexible and careful reparameterizations allows a broader and interpretable overview of the policies and contraceptive preferences in India.