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首页> 外文期刊>Australian & New Zealand journal of statistics >Sequential imputation for models with latent variables assuming latent ignorability
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Sequential imputation for models with latent variables assuming latent ignorability

机译：假设潜在可忽略性的具有潜在变量的模型的顺序插补

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摘要

Models that involve an outcome variable, covariates, and latent variables are frequently the target for estimation and inference. The presence of missing covariate or outcome data presents a challenge, particularly when missingness depends on the latent variables. This missingness mechanism is called latent ignorable or latent missing at random and is a generalisation of missing at random. Several authors have previously proposed approaches for handling latent ignorable missingness, but these methods rely on prior specification of the joint distribution for the complete data. In practice, specifying the joint distribution can be difficult and/or restrictive. We develop a novel sequential imputation procedure for imputing covariate and outcome data for models with latent variables under latent ignorable missingness. The proposed method does not require a joint model; rather, we use results under a joint model to inform imputation with less restrictive modelling assumptions. We discuss identifiability and convergence-related issues, and simulation results are presented in several modelling settings. The method is motivated and illustrated by a study of head and neck cancer recurrence. Imputing missing data for models with latent variables under latent-dependent missingness without specifying a full joint model.
机译：涉及结果变量，协变量和潜变量的模型通常是估计和推断的目标。缺少协变量或结果数据的存在提出了挑战，尤其是当缺失取决于潜在变量时。这种缺失机制称为潜在可忽略或潜在随机缺失，是随机缺失的一般化。先前有几位作者提出了处理潜在的可忽略的缺失的方法，但是这些方法依赖于联合分布的先验规范来获取完整数据。在实践中，指定联合分布可能很困难和/或有限制。我们开发了一种新颖的顺序插补程序，用于在潜在可忽略的缺失下为具有潜在变量的模型插补协变量和结果数据。所提出的方法不需要联合模型。相反，我们使用联合模型下的结果，以较少限制的建模假设为插补提供信息。我们讨论了可识别性和与收敛有关的问题，并在几种建模设置中给出了仿真结果。该方法是通过研究头颈癌复发来激发和说明的。在依赖潜在项的缺失下为具有潜在变量的模型估算缺失数据，而无需指定完整的联合模型。

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