对含结构零值的成分数据进行建模
Modelling compositional data with structural zero values
- Umm Al–Qura University（乌姆·古拉大学）
- University of Crete（克里特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对含结构零值的成分数据，提出条件逻辑正态分布模型，结合EM算法实现快速计算，还给出回归设置并与Dirichlet类似模型对比，为成分数据的建模分析提供了新方法。
AI中文摘要：
成分数据是和为1的正多元数据，分析这类数据的常用方法是对数比率变换，但存在零值时该方法不适用。本文提出一种适用于含结构零值的成分数据的条件逻辑正态分布，该模型可应用于任意维度，EM算法保证其快速实现，还给出了回归设置并与Dirichlet类似模型进行了比较。
英文摘要：
Compositional data are positive multivariate data whose sum equals 1. A popular method to analyze such data is via log--ratio transformations, which are however not applicable when zero values are present. In this paper we present a conditional logistic normal distribution suitable for compositional data with structural zero values. The model is applicable to arbitrary dimensions and the EM algorithm guarantees a fast implementation. The model accepts predictors and we provide a residual analysis. We further provide principal component analysis and biplots for visualization of compositional data with zero values present. Simulation studies and real data examples illustrate the performance of the model, with and without covariates, and compare it to the Dirichlet analogue.
