Multiple Imputation of Hierarchical Nonlinear Time Series Data with an Application to School Enrollment Data
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层级非线性时间序列的多重插补及其入学率应用
- 研究问题
- 面对跨国家、跨时期调查覆盖不均，如何在辅助变量关系非线性、插补与分析模型不相容时处理入学率缺失？
- 方法与思路
- 作者提出贝叶斯多重插补方法，通过联合分布的序贯分解建模，并用平滑样条刻画变量之间的非线性关系。
- 材料陈述的贡献
- 摘要称，模拟与中学入学率应用显示，该方法可改善不相容分析模型的参数估计，以及个体缺失值的预测。
- 阅读关注点
- 应核对缺失机制、层级与时间依赖设定、样条和先验选择，以及插补不确定性如何传递至后续分析模型。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明缺失机制假设、改进幅度和适用边界。
原文摘要
International comparisons of hierarchical time series data sets based on survey data, such as annual country-level estimates of school enrollment rates, can suffer from large amounts of missing data due to differing coverage of surveys across countries and across times. A popular approach to handling missing data in these settings is through multiple imputation, which can be especially effective when there is an auxiliary variable that is strongly predictive of and has a smaller amount of missing data than the variable of interest. However, standard methods for multiple imputation of hierarchical time series data can perform poorly when the auxiliary variable and the variable of interest have a nonlinear relationship. Performance can also suffer if the multiple imputations are used to estimate an analysis model that makes different assumptions about the data compared to the imputation model, leading to uncongeniality between analysis and imputation models. We propose a Bayesian method for multiple imputation of hierarchical nonlinear time series data that uses a sequential decomposition of the joint distribution and incorporates smoothing splines to account for nonlinear relationships between variables. We compare the proposed method with existing multiple imputation methods through a simulation study and an application to secondary school enrollment data. We find that the proposed method can lead to substantial performance increases for estimation of parameters in uncongenial analysis models and for prediction of individual missing values.