Bedrick, Lapodus和Powell(2000)提出利用常態潛在變數模型(normal latent variable model)，估計連續與順序變數混合型資料(mixed data)馬氏距離(Mahalanobis Distance)的方法，在本論文中沿用相同方法來估計具遺漏值混合型資料馬氏距離，利用一般位置模型(general location model)進行多重插補(multiple imputation)的方法，藉由模擬資料與實例分析，來評估此方法用於處理估計具遺漏值混合型資料馬氏距離。

Bedrick, Lapodus, and Powell(2000) apply the normal latent variable model to estimate the Mahalanobis distances for mixed continuous and ordinal data. In this thesis, we extend the similar idea by applying general location model and multiple imputation to estimate the Mahalanobis distances for mixed countinuous and ordinal data with missing value. Simulation and real data are used to evaluate the proposed method.

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