通过零删失多元正态模型对成分数据中的结构零进行建模
Modelling structural zeros in compositional data via a zero-censored multivariate normal model
AI总结：
研究如何分析含结构零的成分数据，受前人启发提出新模型，以不同方式处理零值，通过投影到对应边并拟合零删失多元模型来分析成分数据。
摘要：
我们提出一种用于分析带有结构零的成分数据的新模型。受巴特勒（2008 年）在数据存在零值时所提模型启发，我们提出的模型以不同方式处理零值。不是将每个零值投影到顶点，而是投影到其对应边并拟合零删失多元模型。
英文摘要：
Compositional data are multivariate data constrained to lie within the simplex. When zero values are present, most methods fail to apply unless a zero value replacement is performed prior to the analysis. In this paper we present a new model for analyzing compositional data with zero values present. Unlike the model of \cite{butler2008} that projects every zero value towards a vertex, we project them onto their corresponding edge and assume that zero values are censored values from a multivariate normal distribution. We utilize the EM algorithm to estimate the model parameters, in a computationally efficient manner. Simulated and real data illustrate the performance of the proposed model.
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。
