Instrumental variable estimation for compositional treatments by Elisabeth Ailer, Christian L. Müller, Niki Kilbertus

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Compositional data analysis enables statistical rigor in comparative glycomics by Alexander R. Bennett, Jon Lundstrøm, Sayantani Chatterjee, Morten Thaysen-Andersen, Daniel Bojar

“…Abstract Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. …”
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Compositional Data Analysis (CoDA) of Clinopyroxene From Abyssal Peridotites by I. Nishio, K. Itano, P. Waterton, A. Tamura, K. Szilas, T. Morishita

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RELPRON: A Relative Clause Evaluation Data Set for Compositional Distributional Semantics by Laura Rimell, Jean Maillard, Tamara Polajnar, Stephen Clark

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Bayesian compositional generalized linear mixed models for disease prediction using microbiome data by Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi

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The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation by Sarah Brinkley, Jenny J. Gallo-Franco, Natalia Vázquez-Manjarrez, Juliana Chaura, Naa K. A. Quartey, Sahar B. Toulabi, Melanie T. Odenkirk, Eva Jermendi, Marie-Angélique Laporte, Herman E. Lutterodt, Reginald A. Annan, Mariana Barboza, Endale Amare, Warangkana Srichamnong, Andres Jaramillo-Botero, Andres Jaramillo-Botero, Gina Kennedy, Jaclyn Bertoldo, Jessica E. Prenni, Maya Rajasekharan, John de la Parra, Selena Ahmed

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Artificial intelligence can recognize metallic glasses in vast compositional space with sparse data by Weijie Xie, Yitao Sun, Chao Wang, Mingxing Li, Fucheng Li, Yanhui Liu

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Bayesian Hierarchical Compositional Models for Analysing Longitudinal Abundance Data from Microbiome Studies by I. Creus Martí, A. Moya, F. J. Santonja

“…In this paper, we develop a Bayesian model for microbiota longitudinal data, based on Dirichlet distribution with time-varying parameters, that take into account the compositional paradigm and consider principal balances. …”
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Compositional transformations can reasonably introduce phenotype-associated values into sparse features by George I. Austin, Tal Korem

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The changing socioeconomic composition of the Finnish prison population by Mikko Aaltonen, Joonas Pitkänen, Sasu Tyni, Pekka Martikainen

“…CONTRIBUTION: We provide new evidence on changes in the socioeconomic composition of the Finnish prison population. These results are important for prison policy and rehabilitation efforts that seek to improve prisoners’ employability. …”
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Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index by Junseop Oh, Kyoung-Ho Kim, Ho-Rim Kim, Sunhwa Park, Seong-Taek Yun

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Dataset: Compositional analysis and hydrothermal liquefaction of a high-ash microalgae biofilmMendeley Data by Jacob D. Watkins, Hamza Abdellaoui, Elise Barton, Clayton Lords, Ronald C. Sims

“…This dataset contains biochemical composition data and hydrothermal liquefaction (HTL) yield results for a high-ash microalgae biofilm which was cultivated in effluent from a mesophilic anaerobic digester using polyethylene rotating algae biofilm reactors (RABRs). …”
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Composite GDP nowcasting using macroeconomic variables and electricity data by Zhiqiang Lan, Zidi Liu, Guoyao Wu

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Composite likelihood inference for analysis of individual animal identification data by Xueli Xu, Xiaoyue Zhang, Hal Whitehead, Dehan Kong, Ximing Xu

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Composite GDP nowcasting using macroeconomic variables and electricity data. by Zhiqiang Lan, Zidi Liu, Guoyao Wu

“…In this paper, we propose a composite GDP nowcasting model that combines predictions from a dynamic factor model using mixed-frequency macroeconomic indicators and a regression model using real-time electricity data. …”
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COMPOSITE MODEL ERROR SOURCES IN DISCRETE CHANNELS OF DATA CONVERSION by Yu. D. Ivanov, B. V. Lozka, Е. O. Kozljuk

“…Such an approach to errorstream naturally corresponds to the nature of the formation of errors in real data conversion channels. In addition to this, the correct determination of the length of burst errors in the composite model of the source of the error allows maximum use of corrective properties of the codes with the concurrence of the lengths of codewords and error packets, correctable codes.…”
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Compositional and Machine Learning Tools to Model Plant Nutrition: Overview and Perspectives by Léon Etienne Parent

Subjects: “…compositional data analysis…”
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Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model by Matthew D. Koslovsky

Subjects: “…Compositional…”
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Data Leveling of Multi-Map Geochemical Exploration Data Using Compositional Data Analysis: A Case Study from the Baiyinchagan–Maodeng Area, Inner Mongolia, China by Rui Tang, Cheng Li, Keyan Xiao, Guodong Tang

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Comparison between compositional data analysis and principal component analysis for identifying dietary patterns associated with hyperuricemia by Junkang Zhao, Junkang Zhao, Yajie Zhao, Jiannan Han, Jiannan Han, Yixuan Zhao, Sumiao Liu, Zhida Liu, Liyun Zhang, Yan Zhang

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