Is it who you are or where you are? Accounting for compositional differences in cross-site treatment variation

Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables

Deterministic variables are variables that are fully explained by one or more parent variables. They commonly arise when a variable has been algebraically constructed from one or more parent variables, as with composite variables, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic variables may be depicted within directed acyclic graphs (DAGs) to help with identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables. We propose a two-step approach in whic…

行业层面的会计报表分析：组合方法的温和介绍

Accounting statement analysis at industry level. A gentle introduction to the compositional approach

本文介绍组合数据分析方法在行业层面财务报表分析中的应用，通过几何均值计算行业财务比率均值，利用组合主成分分析、聚类分析和回归模型进行可视化与建模，并以西班牙酒庄为例演示杜邦分析。

Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors

Visual objects are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance. However, object appearance is the compositional consequence of both an object's intrinsic and extrinsic causal factors, where the extrinsic causal factors are related to illumination, and imaging conditions. Therefore, this paper proposes a unified tensor model of wholes and parts, and introduces a compositional hierarchical tensor factorization that disentangles the hierarchical ca…

Generalised functional additive mixed models with compositional covariates for areal Covid-19 incidence curves

We extend the generalised functional additive mixed model to include (functional) compositional covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a subspace of the $L^2$, we include functional compositions as transformed functional covariates with constrained effect function. The extended model allows for the estimation of linear, nonlinear and time-varying effects of scalar and functional covariates, as well as (correlated) functional random effects, in addition to the compositional effects. We us…

狄利克雷分布上的空间自回归模型

Spatial Autoregressive Model on a Dirichlet Distribution

针对成分数据忽略空间依赖的问题，提出空间自回归狄利克雷回归模型，整合空间滞后项并构建最大似然估计，在合成和真实数据上验证其更准确可靠。

一种基于树的模型，用于解决微生物组组成计数数据中的稀疏性和分类群协方差问题

A tree-based model for addressing sparsity and taxa covariance in microbiome compositional count data

本文提出logistic-tree normal（LTN）生成模型，通过基于树的分解和多元logistic-normal分布灵活刻画微生物组数据的稀疏性与分类群协方差，并利用共轭分块Gibbs采样实现高效贝叶斯推断，在差异丰度分析中无需显式零膨胀成分即可准确生成零值比例。