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Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric

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Bibliographic Details
Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research
description
discipline_display e-Learning
discipline_facet e-Learning
format Online Article
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genre Journal Article
id rss_article:57204
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
e-Learning
General
e-Learning
sub_discipline_display General
sub_discipline_facet General
subject_display e-Learning
General
e-Learning
e-Learning
General
e-Learning
subject_facet e-Learning
General
e-Learning
title Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_auth Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_full Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_fullStr Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_full_unstemmed Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_short Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
title_sort projected statistical methods for distributional data on the real line with the wasserstein metric
topic e-Learning
General
e-Learning
url http://jmlr.org/papers/v23/21-0059.html