用于具有零膨胀的多变量二元数据的潜变量模型：家庭支持代际交换分析
Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support
AI总结：
本文提出结合连续潜变量与二元潜类别变量的多变量零膨胀潜变量模型，用MCMC和定制数据增广估计，并基于英国家庭纵向研究分析代际家庭支持交换。
摘要：
了解不同代际家庭成员之间交换的帮助和支持正变得日益重要，社会学和社会政策中的研究问题既关注给予和接受帮助水平的预测因素，也关注二者之间的互惠性。我们提出用于分析此类数据的通用潜变量模型，其中每个方向的帮助倾向由特定帮助类型的多个二元指标测量。该模型将两个代表帮助倾向的连续潜变量与两个二元潜类别变量相结合，后者允许存在高比例的未给予或未接受任何帮助的回答。这定义了零膨胀模型的一个多变量版本。模型的主要部分采用 MCMC 方法并配合定制的数据增广算法进行估计。我们使用英国家庭纵向研究（UK Household Longitudinal Study）的调查数据，应用这些模型分析成年个体与其非同住父母之间的帮助交换。
英文摘要：
Understanding the help and support that is exchanged between family members of different generations is of increasing importance, with research questions in sociology and social policy focusing on both predictors of the levels of help given and received, and on reciprocity between them. We propose general latent variable models for analysing such data, when helping tendencies in each direction are measured by multiple binary indicators of specific types of help. The model combines two continuous latent variables, which represent the helping tendencies, with two binary latent class variables which allow for high proportions of responses where no help of any kind is given or received. This defines a multivariate version of a zero inflation model. The main part of the models is estimated using MCMC methods, with a bespoke data augmentation algorithm. We apply the models to analyse exchanges of help between adult individuals and their non-coresident parents, using survey data from the UK Household Longitudinal Study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
