Gelman, King, 及Liu(1998)針對一連串且互相獨立的橫斷面調查提出多重設算程序，且對不同調查的參數以階層模式(hierarchical model)連結。本文為介紹複雜抽樣(分層或群集抽樣)之下，若Q個連續變數有遺漏現象時，如何結合對象之個別特性，各層或各群集的參數，以及連結各層或各群集參數的階層模式，以設算遺漏值及估計模式中之參數。

對遺漏值的處理採用單調資料擴展演算法，只需對破壞單調資料型態的遺漏值進行設算。由於考慮到不同的群集或層往往呈現不同的特性，因而以階層模式連絡各群集或各層的參數，並將Gelman, King, Liu(1998)的推導結果擴展到將個別對象之特性納入考量之上。對各群集而言，他們的共變異數矩陣Ψ及Σ為影響群內其他參數的收斂情形，由模擬獲得的結果，沒有證據顯示應懷疑收斂的問題。

Gelman, king, and Liu (1998) use multiple imputation for a series of cross section survey, and link the parameter of different survey by hierarchical model. This text introduces a method to impute missing value and estimate the parameters affected by hierarchical model if Q continuous variables has missing value under complex survey.

For each cluster, the parameters are influenced by their variance-covariance matrix Ψ and Σ. The result obtained from the simulation have no clear evidence to doubt the convergence of parameters.

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