Imprecise Compositional Data Analysis: Alternative Statistical Methods

Michael Smithson
Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:364-366, 2019.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v103-smithson19a, title = {Imprecise Compositional Data Analysis: Alternative Statistical Methods}, author = {Smithson, Michael}, booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {364--366}, year = {2019}, editor = {De Bock, Jasper and de Campos, Cassio P. and de Cooman, Gert and Quaeghebeur, Erik and Wheeler, Gregory}, volume = {103}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v103/smithson19a/smithson19a.pdf}, url = {https://proceedings.mlr.press/v103/smithson19a.html}, 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.} }
Endnote
%0 Conference Paper %T Imprecise Compositional Data Analysis: Alternative Statistical Methods %A Michael Smithson %B Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2019 %E Jasper De Bock %E Cassio P. de Campos %E Gert de Cooman %E Erik Quaeghebeur %E Gregory Wheeler %F pmlr-v103-smithson19a %I PMLR %P 364--366 %U https://proceedings.mlr.press/v103/smithson19a.html %V 103 %X 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.
APA
Smithson, M.. (2019). Imprecise Compositional Data Analysis: Alternative Statistical Methods. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 103:364-366 Available from https://proceedings.mlr.press/v103/smithson19a.html.

Related Material