Fitting mixed models to complex survey data is a challenging problem. Most methods in the literature, including the most widely used one, require a close relationship between the model structure and the survey design. In this paper we present methods for fitting arbitrary mixed models to data from arbitrary survey designs. We support this with an implementation that allows for multilevel linear models and multistage designs without any assumptions about nesting of model and design, and that also allows for correlation structures such as those resulting from genetic relatedness. The estimation and inference approach uses weighted pairwise (composite) likelihood.

翻译：将混合模型拟合至复杂调查数据是一项具有挑战性的问题。文献中的大部分方法，包括应用最广泛的方法，均要求模型结构与调查设计之间存在紧密关联。本文提出了将任意混合模型拟合至任意调查设计数据的方法，并提供了支持多层级线性模型与多阶段设计的实现方案（不要求模型与设计具有嵌套关系），同时可处理基因亲缘关系等产生的相关结构。估计与推断方法采用加权成对（复合）似然。