A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation
The advent of data science has spurred interest in estimating properties of distributions over large alphabets. Fundamental symmetric properties such as support size, support coverage, entropy, and proximity to uniformity, received most attention, with each property estimated using a different techn...
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| creator | Acharya, Jayadev Das, Hirakendu Orlitsky, Alon Suresh, Ananda Theertha |
| description | The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties. |
| doi_str_mv | 10.48550/arxiv.1611.02960 |
| format | Article |
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distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.</description><identifier>DOI: 10.48550/arxiv.1611.02960</identifier><language>eng</language><subject>Computer Science - Data Structures and Algorithms ; Computer Science - Information Theory ; Computer Science - Learning ; Mathematics - Information Theory</subject><creationdate>2016-11</creationdate><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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1611.02960$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1611.02960$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Acharya, Jayadev</creatorcontrib><creatorcontrib>Das, Hirakendu</creatorcontrib><creatorcontrib>Orlitsky, Alon</creatorcontrib><creatorcontrib>Suresh, Ananda Theertha</creatorcontrib><title>A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation</title><description>The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.</description><subject>Computer Science - Data Structures and Algorithms</subject><subject>Computer Science - Information Theory</subject><subject>Computer Science - Learning</subject><subject>Mathematics - Information Theory</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj0tqwzAYhLXpoqQ9QFfVBezqLXlp0vQBLiklWZtflkxE7MjITklu3zjNamCGmeFD6ImSXBgpyQukU_jNqaI0J6xQ5B79lHh7CG3wDn_BKfTHHldh77uwi9HhchhShGaH25jwephCDx1-DeOUgj1OIR7wd4qDT9MZr8Y5nb0HdNdCN_rHmy7Q5m21WX5k1fr9c1lWGShNstYxYwvhgFoJwJVuhIbCNtoyr4hjyhsCDRjOGJXMcWmEsIyAFJ57fWks0PP_7BWqHtLlPp3rGa6-wvE_MUJK5Q</recordid><startdate>20161109</startdate><enddate>20161109</enddate><creator>Acharya, Jayadev</creator><creator>Das, Hirakendu</creator><creator>Orlitsky, Alon</creator><creator>Suresh, Ananda Theertha</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20161109</creationdate><title>A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation</title><author>Acharya, Jayadev ; Das, Hirakendu ; Orlitsky, Alon ; Suresh, Ananda Theertha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-fd28b94da1b5aa367c47a9bc7b2e60d26e80aca8322152d35844b20a54e3e7aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Computer Science - Data Structures and Algorithms</topic><topic>Computer Science - Information Theory</topic><topic>Computer Science - Learning</topic><topic>Mathematics - Information Theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Acharya, Jayadev</creatorcontrib><creatorcontrib>Das, Hirakendu</creatorcontrib><creatorcontrib>Orlitsky, Alon</creatorcontrib><creatorcontrib>Suresh, Ananda Theertha</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Acharya, Jayadev</au><au>Das, Hirakendu</au><au>Orlitsky, Alon</au><au>Suresh, Ananda Theertha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation</atitle><date>2016-11-09</date><risdate>2016</risdate><abstract>The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.</abstract><doi>10.48550/arxiv.1611.02960</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Data Structures and Algorithms Computer Science - Information Theory Computer Science - Learning Mathematics - Information Theory |
| title | A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation |
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