ATS Methods: Nonparametric Regression for Non-Gaussian Data
ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the "A" in ATS) is carried out, then a variance-stabilizing transformation is applied ("T&qu...
Gespeichert in:
| Veröffentlicht in: | Journal of the American Statistical Association 1993-09, Vol.88 (423), p.821-835 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 835 |
|---|---|
| container_issue | 423 |
| container_start_page | 821 |
| container_title | Journal of the American Statistical Association |
| container_volume | 88 |
| creator | Cleveland, William S. Mallows, Colin L. McRae, Jean E. |
| description | ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the "A" in ATS) is carried out, then a variance-stabilizing transformation is applied ("T"), and finally the result is smoothed ("S") using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface when the response is binary, for estimating density, and for estimating the spectrum of a time series. We also present some theoretical investigations that give guidance on how to choose the amount of averaging and how efficient the methods are. |
| doi_str_mv | 10.1080/01621459.1993.10476347 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_journals_274797195</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>2290771</jstor_id><sourcerecordid>2290771</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-1fbd9f3e57401a111ff89304e19c3657b1d8aa3d4faf0ea6494473dd15f2ca1e3</originalsourceid><addsrcrecordid>eNqFkNtKxDAQhoMouB5eQYqId9VMJ20avVo8gwfwAN6FsU20S7dZky7i25uyrog3zs3AzDc_w8fYDvAD4CU_5FBkIHJ1AEphHAlZoJArbAQ5yjST4nmVjQYoHah1thHChMeSZTlix-PHh-TG9G-uDkfJretm5Glqet9Uyb159SaExnWJdX5Yphc0jwPqklPqaYutWWqD2f7um-zp_Ozx5DK9vru4OhlfpxXKvE_BvtTKosml4EAAYG2pkAsDqsIily9Ql0RYC0uWGyqEEkJiXUNus4rA4CbbX-TOvHufm9DraRMq07bUGTcPGkshFOYYwd0_4MTNfRd_01GDVBJUHqFiAVXeheCN1TPfTMl_auB6EKqXQvUgVC-FxsO973QKFbXWU1c14ecapUChfmGT0Dv_OzxDLnWWKS4lRGy8wJouyp3Sh_NtrXv6bJ1fRuM_H30BKkiTZg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>274797195</pqid></control><display><type>article</type><title>ATS Methods: Nonparametric Regression for Non-Gaussian Data</title><source>Jstor Complete Legacy</source><source>JSTOR Mathematics and Statistics</source><creator>Cleveland, William S. ; Mallows, Colin L. ; McRae, Jean E.</creator><creatorcontrib>Cleveland, William S. ; Mallows, Colin L. ; McRae, Jean E.</creatorcontrib><description>ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the "A" in ATS) is carried out, then a variance-stabilizing transformation is applied ("T"), and finally the result is smoothed ("S") using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface when the response is binary, for estimating density, and for estimating the spectrum of a time series. We also present some theoretical investigations that give guidance on how to choose the amount of averaging and how efficient the methods are.</description><identifier>ISSN: 0162-1459</identifier><identifier>EISSN: 1537-274X</identifier><identifier>DOI: 10.1080/01621459.1993.10476347</identifier><identifier>CODEN: JSTNAL</identifier><language>eng</language><publisher>Alexandria, VA: Taylor & Francis Group</publisher><subject>Data smoothing ; Density estimation ; Estimation bias ; Estimation methods ; Exact sciences and technology ; Linear regression ; Loess ; Logarithms ; Mathematics ; Nonhomogeneous Poisson processes ; Nonparametric inference ; Probability and statistics ; Regression analysis ; Sciences and techniques of general use ; Spectrum estimation ; Statistical variance ; Statistics ; Telephones ; Theory and Methods ; Time series</subject><ispartof>Journal of the American Statistical Association, 1993-09, Vol.88 (423), p.821-835</ispartof><rights>Copyright Taylor & Francis Group, LLC 1993</rights><rights>Copyright 1993 American Statistical Association</rights><rights>1994 INIST-CNRS</rights><rights>Copyright American Statistical Association Sep 1993</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-1fbd9f3e57401a111ff89304e19c3657b1d8aa3d4faf0ea6494473dd15f2ca1e3</citedby><cites>FETCH-LOGICAL-c375t-1fbd9f3e57401a111ff89304e19c3657b1d8aa3d4faf0ea6494473dd15f2ca1e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2290771$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2290771$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27924,27925,58017,58021,58250,58254</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3743497$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Cleveland, William S.</creatorcontrib><creatorcontrib>Mallows, Colin L.</creatorcontrib><creatorcontrib>McRae, Jean E.</creatorcontrib><title>ATS Methods: Nonparametric Regression for Non-Gaussian Data</title><title>Journal of the American Statistical Association</title><description>ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the "A" in ATS) is carried out, then a variance-stabilizing transformation is applied ("T"), and finally the result is smoothed ("S") using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface when the response is binary, for estimating density, and for estimating the spectrum of a time series. We also present some theoretical investigations that give guidance on how to choose the amount of averaging and how efficient the methods are.</description><subject>Data smoothing</subject><subject>Density estimation</subject><subject>Estimation bias</subject><subject>Estimation methods</subject><subject>Exact sciences and technology</subject><subject>Linear regression</subject><subject>Loess</subject><subject>Logarithms</subject><subject>Mathematics</subject><subject>Nonhomogeneous Poisson processes</subject><subject>Nonparametric inference</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sciences and techniques of general use</subject><subject>Spectrum estimation</subject><subject>Statistical variance</subject><subject>Statistics</subject><subject>Telephones</subject><subject>Theory and Methods</subject><subject>Time series</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkNtKxDAQhoMouB5eQYqId9VMJ20avVo8gwfwAN6FsU20S7dZky7i25uyrog3zs3AzDc_w8fYDvAD4CU_5FBkIHJ1AEphHAlZoJArbAQ5yjST4nmVjQYoHah1thHChMeSZTlix-PHh-TG9G-uDkfJretm5Glqet9Uyb159SaExnWJdX5Yphc0jwPqklPqaYutWWqD2f7um-zp_Ozx5DK9vru4OhlfpxXKvE_BvtTKosml4EAAYG2pkAsDqsIily9Ql0RYC0uWGyqEEkJiXUNus4rA4CbbX-TOvHufm9DraRMq07bUGTcPGkshFOYYwd0_4MTNfRd_01GDVBJUHqFiAVXeheCN1TPfTMl_auB6EKqXQvUgVC-FxsO973QKFbXWU1c14ecapUChfmGT0Dv_OzxDLnWWKS4lRGy8wJouyp3Sh_NtrXv6bJ1fRuM_H30BKkiTZg</recordid><startdate>19930901</startdate><enddate>19930901</enddate><creator>Cleveland, William S.</creator><creator>Mallows, Colin L.</creator><creator>McRae, Jean E.</creator><general>Taylor & Francis Group</general><general>American Statistical Association</general><general>Taylor & Francis Ltd</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8BJ</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>K9-</scope><scope>K9.</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M0R</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>19930901</creationdate><title>ATS Methods: Nonparametric Regression for Non-Gaussian Data</title><author>Cleveland, William S. ; Mallows, Colin L. ; McRae, Jean E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-1fbd9f3e57401a111ff89304e19c3657b1d8aa3d4faf0ea6494473dd15f2ca1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Data smoothing</topic><topic>Density estimation</topic><topic>Estimation bias</topic><topic>Estimation methods</topic><topic>Exact sciences and technology</topic><topic>Linear regression</topic><topic>Loess</topic><topic>Logarithms</topic><topic>Mathematics</topic><topic>Nonhomogeneous Poisson processes</topic><topic>Nonparametric inference</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sciences and techniques of general use</topic><topic>Spectrum estimation</topic><topic>Statistical variance</topic><topic>Statistics</topic><topic>Telephones</topic><topic>Theory and Methods</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cleveland, William S.</creatorcontrib><creatorcontrib>Mallows, Colin L.</creatorcontrib><creatorcontrib>McRae, Jean E.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI商业信息数据库</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM global</collection><collection>ProQuest Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Healthcare Administration Database</collection><collection>Medical Database</collection><collection>ProQuest Research Library</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</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>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cleveland, William S.</au><au>Mallows, Colin L.</au><au>McRae, Jean E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ATS Methods: Nonparametric Regression for Non-Gaussian Data</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1993-09-01</date><risdate>1993</risdate><volume>88</volume><issue>423</issue><spage>821</spage><epage>835</epage><pages>821-835</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><coden>JSTNAL</coden><abstract>ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the "A" in ATS) is carried out, then a variance-stabilizing transformation is applied ("T"), and finally the result is smoothed ("S") using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface when the response is binary, for estimating density, and for estimating the spectrum of a time series. We also present some theoretical investigations that give guidance on how to choose the amount of averaging and how efficient the methods are.</abstract><cop>Alexandria, VA</cop><pub>Taylor & Francis Group</pub><doi>10.1080/01621459.1993.10476347</doi><tpages>15</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0162-1459 |
| ispartof | Journal of the American Statistical Association, 1993-09, Vol.88 (423), p.821-835 |
| issn | 0162-1459 1537-274X |
| language | eng |
| recordid | cdi_proquest_journals_274797195 |
| source | Jstor Complete Legacy; JSTOR Mathematics and Statistics |
| subjects | Data smoothing Density estimation Estimation bias Estimation methods Exact sciences and technology Linear regression Loess Logarithms Mathematics Nonhomogeneous Poisson processes Nonparametric inference Probability and statistics Regression analysis Sciences and techniques of general use Spectrum estimation Statistical variance Statistics Telephones Theory and Methods Time series |
| title | ATS Methods: Nonparametric Regression for Non-Gaussian Data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T09%3A17%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ATS%20Methods:%20Nonparametric%20Regression%20for%20Non-Gaussian%20Data&rft.jtitle=Journal%20of%20the%20American%20Statistical%20Association&rft.au=Cleveland,%20William%20S.&rft.date=1993-09-01&rft.volume=88&rft.issue=423&rft.spage=821&rft.epage=835&rft.pages=821-835&rft.issn=0162-1459&rft.eissn=1537-274X&rft.coden=JSTNAL&rft_id=info:doi/10.1080/01621459.1993.10476347&rft_dat=%3Cjstor_proqu%3E2290771%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=274797195&rft_id=info:pmid/&rft_jstor_id=2290771&rfr_iscdi=true |