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Imprecise compositional data analysis: Alternative statistical methods

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Date

2019

Authors

Smithson, Michael

Journal Title

Journal ISSN

Volume Title

Publisher

Proceedings of Machine Learning Research

Abstract

This paper briefly describes statistical methods for analyzing imprecise compositional data that might be elicited from approximate measurement or from expert judgments. Two alternative approaches are discussed: Log-ratio transforms and probability-ratio transforms. The first is well-established and the second is under development by the author. The primary focus in this paper is on generalized linear models for predicting imprecise compositional data.

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Keywords

Citation

URI

http://hdl.handle.net/1885/203833

Collections

ANU Research Publications

Source

Type

Conference paper

Book Title

Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications ISIPTA 2019

Entity type

Access Statement

Open Access

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DOI

Restricted until

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