High-Dimensional Adaptive Basis Density Estimation ...
In the realm of high-dimensional statistics, regression and classification have received much attention, while density estimation has lagged behind. Yet there are compelling scientific questions which can only be addressed via density estimation using high-dimensional data, such as the paths of Nort...
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Carnegie Mellon University
2011
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ftdatacite:10.1184/r1/6719834.v1 2023-08-27T04:10:52+02:00 High-Dimensional Adaptive Basis Density Estimation ... Buchman, Susan 2011 https://dx.doi.org/10.1184/r1/6719834.v1 https://kilthub.cmu.edu/articles/High-Dimensional_Adaptive_Basis_Density_Estimation/6719834/1 unknown Carnegie Mellon University https://dx.doi.org/10.1184/r1/6719834 In Copyright http://rightsstatements.org/vocab/InC/1.0/ Probability Statistics FOS Mathematics Text article-journal Thesis ScholarlyArticle 2011 ftdatacite https://doi.org/10.1184/r1/6719834.v110.1184/r1/6719834 2023-08-07T14:24:23Z In the realm of high-dimensional statistics, regression and classification have received much attention, while density estimation has lagged behind. Yet there are compelling scientific questions which can only be addressed via density estimation using high-dimensional data, such as the paths of North Atlantic tropical cyclones. If we cast each track as a single high-dimensional data point, density estimation allows us to answer such questions via integration or Monte Carlo methods. In this dissertation, I present three new methods for estimating densities and intensities for high-dimensional data, all of which rely on a technique called diffusion maps. This technique constructs a mapping for high-dimensional, complex data into a low-dimensional space, providing a new basis that can be used in conjunction with traditional density estimation methods. Furthermore, I propose a reordering of importance sampling in the high-dimensional setting. Traditional importance sampling estimates high-dimensional integrals ... Text North Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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Probability Statistics FOS Mathematics |
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Probability Statistics FOS Mathematics Buchman, Susan High-Dimensional Adaptive Basis Density Estimation ... |
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Probability Statistics FOS Mathematics |
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In the realm of high-dimensional statistics, regression and classification have received much attention, while density estimation has lagged behind. Yet there are compelling scientific questions which can only be addressed via density estimation using high-dimensional data, such as the paths of North Atlantic tropical cyclones. If we cast each track as a single high-dimensional data point, density estimation allows us to answer such questions via integration or Monte Carlo methods. In this dissertation, I present three new methods for estimating densities and intensities for high-dimensional data, all of which rely on a technique called diffusion maps. This technique constructs a mapping for high-dimensional, complex data into a low-dimensional space, providing a new basis that can be used in conjunction with traditional density estimation methods. Furthermore, I propose a reordering of importance sampling in the high-dimensional setting. Traditional importance sampling estimates high-dimensional integrals ... |
format |
Text |
author |
Buchman, Susan |
author_facet |
Buchman, Susan |
author_sort |
Buchman, Susan |
title |
High-Dimensional Adaptive Basis Density Estimation ... |
title_short |
High-Dimensional Adaptive Basis Density Estimation ... |
title_full |
High-Dimensional Adaptive Basis Density Estimation ... |
title_fullStr |
High-Dimensional Adaptive Basis Density Estimation ... |
title_full_unstemmed |
High-Dimensional Adaptive Basis Density Estimation ... |
title_sort |
high-dimensional adaptive basis density estimation ... |
publisher |
Carnegie Mellon University |
publishDate |
2011 |
url |
https://dx.doi.org/10.1184/r1/6719834.v1 https://kilthub.cmu.edu/articles/High-Dimensional_Adaptive_Basis_Density_Estimation/6719834/1 |
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North Atlantic |
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North Atlantic |
op_relation |
https://dx.doi.org/10.1184/r1/6719834 |
op_rights |
In Copyright http://rightsstatements.org/vocab/InC/1.0/ |
op_doi |
https://doi.org/10.1184/r1/6719834.v110.1184/r1/6719834 |
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1775353213315186688 |