Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling
In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust m...
Published in: | Statistical Modelling |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
SAGE Publications
2021
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1177/1471082x211043946 http://journals.sagepub.com/doi/pdf/10.1177/1471082X211043946 http://journals.sagepub.com/doi/full-xml/10.1177/1471082X211043946 |
id |
crsagepubl:10.1177/1471082x211043946 |
---|---|
record_format |
openpolar |
spelling |
crsagepubl:10.1177/1471082x211043946 2024-10-06T13:42:37+00:00 Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling Sheanshang, Daniel M. White, Philip A. Keeler, Durban G. 2021 http://dx.doi.org/10.1177/1471082x211043946 http://journals.sagepub.com/doi/pdf/10.1177/1471082X211043946 http://journals.sagepub.com/doi/full-xml/10.1177/1471082X211043946 en eng SAGE Publications http://journals.sagepub.com/page/policies/text-and-data-mining-license Statistical Modelling volume 23, issue 2, page 151-172 ISSN 1471-082X 1477-0342 journal-article 2021 crsagepubl https://doi.org/10.1177/1471082x211043946 2024-09-10T04:24:52Z In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks. Article in Journal/Newspaper Antarc* Antarctic SAGE Publications Antarctic Statistical Modelling 1471082X2110439 |
institution |
Open Polar |
collection |
SAGE Publications |
op_collection_id |
crsagepubl |
language |
English |
description |
In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks. |
format |
Article in Journal/Newspaper |
author |
Sheanshang, Daniel M. White, Philip A. Keeler, Durban G. |
spellingShingle |
Sheanshang, Daniel M. White, Philip A. Keeler, Durban G. Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
author_facet |
Sheanshang, Daniel M. White, Philip A. Keeler, Durban G. |
author_sort |
Sheanshang, Daniel M. |
title |
Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
title_short |
Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
title_full |
Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
title_fullStr |
Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
title_full_unstemmed |
Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling |
title_sort |
outlier accommodation with semiparametric density processes: a study of antarctic snow density modelling |
publisher |
SAGE Publications |
publishDate |
2021 |
url |
http://dx.doi.org/10.1177/1471082x211043946 http://journals.sagepub.com/doi/pdf/10.1177/1471082X211043946 http://journals.sagepub.com/doi/full-xml/10.1177/1471082X211043946 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Statistical Modelling volume 23, issue 2, page 151-172 ISSN 1471-082X 1477-0342 |
op_rights |
http://journals.sagepub.com/page/policies/text-and-data-mining-license |
op_doi |
https://doi.org/10.1177/1471082x211043946 |
container_title |
Statistical Modelling |
container_start_page |
1471082X2110439 |
_version_ |
1812176325642813440 |