Fast Exact Univariate Kernel Density Estimation
This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2019-11 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Hofmeyr, David P |
| description | This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given an ordered sample the computational complexity is linear in the sample size. Combining the proposed methodology with existing approximation methods results in extremely fast density estimation. Extensive experimentation documents the effectiveness and efficiency of this approach compared with the existing state-of-the-art. |
| doi_str_mv | 10.48550/arxiv.1806.00690 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_1806_00690</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2073859105</sourcerecordid><originalsourceid>FETCH-LOGICAL-a525-520f79d1a5b4f0afc5ba241e80bcbad69dd4a692cc6cc21a662d1ff73ad506ed3</originalsourceid><addsrcrecordid>eNotj01PwkAURScmJhLkB7iyieuWNzN903ZpsKiRxA2um9f5SIZgwZmBwL-3gqu7Obn3HsYeOBRljQhzCid_LHgNqgBQDdywiZCS53UpxB2bxbgBAKEqgSgnbL6kmLL2RDplX4M_UvCUbPZhw2C32Ysdok_nrI3Jf1Pyu-Ge3TraRjv7zylbL9v14i1ffb6-L55XOaHAHAW4qjGcsC8dkNPYkyi5raHXPRnVGFOSaoTWSmvBSSlhuHOVJIOgrJFT9nitvdh0-zDOh3P3Z9VdrEbi6Ursw-7nYGPqNrtDGMZPnYBK1thwQPkLvlNPYg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2073859105</pqid></control><display><type>article</type><title>Fast Exact Univariate Kernel Density Estimation</title><source>Freely Accessible Journals</source><source>arXiv.org</source><creator>Hofmeyr, David P</creator><creatorcontrib>Hofmeyr, David P</creatorcontrib><description>This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given an ordered sample the computational complexity is linear in the sample size. Combining the proposed methodology with existing approximation methods results in extremely fast density estimation. Extensive experimentation documents the effectiveness and efficiency of this approach compared with the existing state-of-the-art.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1806.00690</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Approximation ; Automobile insurance ; Density ; Experimentation ; Extreme values ; Insurance claims ; Kernels ; Methodology ; Sample size ; Statistics - Computation</subject><ispartof>arXiv.org, 2019-11</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27924</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.1806.00690$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1109/TPAMI.2019.2930501$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Hofmeyr, David P</creatorcontrib><title>Fast Exact Univariate Kernel Density Estimation</title><title>arXiv.org</title><description>This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given an ordered sample the computational complexity is linear in the sample size. Combining the proposed methodology with existing approximation methods results in extremely fast density estimation. Extensive experimentation documents the effectiveness and efficiency of this approach compared with the existing state-of-the-art.</description><subject>Approximation</subject><subject>Automobile insurance</subject><subject>Density</subject><subject>Experimentation</subject><subject>Extreme values</subject><subject>Insurance claims</subject><subject>Kernels</subject><subject>Methodology</subject><subject>Sample size</subject><subject>Statistics - Computation</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj01PwkAURScmJhLkB7iyieuWNzN903ZpsKiRxA2um9f5SIZgwZmBwL-3gqu7Obn3HsYeOBRljQhzCid_LHgNqgBQDdywiZCS53UpxB2bxbgBAKEqgSgnbL6kmLL2RDplX4M_UvCUbPZhw2C32Ysdok_nrI3Jf1Pyu-Ge3TraRjv7zylbL9v14i1ffb6-L55XOaHAHAW4qjGcsC8dkNPYkyi5raHXPRnVGFOSaoTWSmvBSSlhuHOVJIOgrJFT9nitvdh0-zDOh3P3Z9VdrEbi6Ursw-7nYGPqNrtDGMZPnYBK1thwQPkLvlNPYg</recordid><startdate>20191109</startdate><enddate>20191109</enddate><creator>Hofmeyr, David P</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20191109</creationdate><title>Fast Exact Univariate Kernel Density Estimation</title><author>Hofmeyr, David P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a525-520f79d1a5b4f0afc5ba241e80bcbad69dd4a692cc6cc21a662d1ff73ad506ed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Approximation</topic><topic>Automobile insurance</topic><topic>Density</topic><topic>Experimentation</topic><topic>Extreme values</topic><topic>Insurance claims</topic><topic>Kernels</topic><topic>Methodology</topic><topic>Sample size</topic><topic>Statistics - Computation</topic><toplevel>online_resources</toplevel><creatorcontrib>Hofmeyr, David P</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hofmeyr, David P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast Exact Univariate Kernel Density Estimation</atitle><jtitle>arXiv.org</jtitle><date>2019-11-09</date><risdate>2019</risdate><eissn>2331-8422</eissn><abstract>This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given an ordered sample the computational complexity is linear in the sample size. Combining the proposed methodology with existing approximation methods results in extremely fast density estimation. Extensive experimentation documents the effectiveness and efficiency of this approach compared with the existing state-of-the-art.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1806.00690</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2019-11 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_arxiv_primary_1806_00690 |
| source | Freely Accessible Journals; arXiv.org |
| subjects | Approximation Automobile insurance Density Experimentation Extreme values Insurance claims Kernels Methodology Sample size Statistics - Computation |
| title | Fast Exact Univariate Kernel Density Estimation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T10%3A41%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fast%20Exact%20Univariate%20Kernel%20Density%20Estimation&rft.jtitle=arXiv.org&rft.au=Hofmeyr,%20David%20P&rft.date=2019-11-09&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.1806.00690&rft_dat=%3Cproquest_arxiv%3E2073859105%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2073859105&rft_id=info:pmid/&rfr_iscdi=true |