Imprecise Compositional Data Analysis: Alternative Statistical Methods

Michael Smithson

Imprecise Compositional Data Analysis: Alternative Statistical Methods

Michael Smithson^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	364-366
Number of pages	3
Journal	Proceedings of Machine Learning Research
Volume	103
Publication status	Published - 2019
Event	11th International Symposium on Imprecise Probabilities: Theories and Applications, ISIPTA 2019 - Ghent, Belgium Duration: 3 Jul 2019 → 6 Jul 2019

Cite this

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title = "Imprecise Compositional Data Analysis: Alternative Statistical Methods",

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.",

keywords = "beta distribution, cdf-quantile distribution, compositional data, copula, general linear model, imprecise data",

author = "Michael Smithson",

note = "Publisher Copyright: {\textcopyright} 2019 M. Smithson.; 11th International Symposium on Imprecise Probabilities: Theories and Applications, ISIPTA 2019 ; Conference date: 03-07-2019 Through 06-07-2019",

year = "2019",

language = "English",

volume = "103",

pages = "364--366",

journal = "Proceedings of Machine Learning Research",

issn = "2640-3498",

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TY - JOUR

T1 - Imprecise Compositional Data Analysis

T2 - 11th International Symposium on Imprecise Probabilities: Theories and Applications, ISIPTA 2019

AU - Smithson, Michael

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - beta distribution

KW - cdf-quantile distribution

KW - compositional data

KW - copula

KW - general linear model

KW - imprecise data

UR - https://www.scopus.com/pages/publications/85125036398

M3 - Conference article

AN - SCOPUS:85125036398

SN - 2640-3498

VL - 103

SP - 364

EP - 366

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

Y2 - 3 July 2019 through 6 July 2019

ER -

Imprecise Compositional Data Analysis: Alternative Statistical Methods

Abstract

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