Analysis of Signals Under Compositional Noise With Applications to SONAR Data

Analysis of High Dimensional Compositional Data Containing Structural Zeros with Applications to Microbiome Data

This paper is motivated by the recent interest in the analysis of high dimensional microbiome data. A key feature of this data is the presence of ‘structural zeros’ which are microbes missing from an observation vector due to an underlying biological process and not due to error in measurement. Typical notions of missingness are insufficient to model these structural zeros. We define a general ...

A Concise Guide to Compositional Data Analysis

Compositional Analysis of Dynamic Bayesian Networks and Applications to CPS

Dynamic Bayesian networks (DBNs) can be effectively used to model various problems in complex dynamic systems. We perform an empirical investigation on compositional analysis of DBNs using abstraction. In static systems and hidden Markov models, computation of a metric called treewidth induces a tree decomposition that can be used to perform logical or probabilistic inference and max+ optimizat...

Compositional Modeling for Data-Centric Business Applications

Data-centric business applications comprise an important class of distributed systems that includes on-line stores, document management systems, and patient portals. However, their complexity makes it difficult to design and implement them. We address these issues from a model-driven perspective by developing a formal, compositional, and domain-specific set of abstractions for the specification...

Correlation Analysis for Compositional Data

Compositional data need a special treatment prior to correlation analysis. In this paper we argue why standard transformations for compositional data are not suitable for computing correlations, and why the use of raw or log-transformed data is neither meaningful. As a solution, a procedure based on balances is outlined, leading to sensible correlation measures. The construction of the balances...