Loading…
Model-free model-fitting and predictive distributions
The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion...
Saved in:
| Published in: | Test (Madrid, Spain) Spain), 2013-06, Vol.22 (2), p.183-221 |
|---|---|
| Main Author: | |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303 |
| container_end_page | 221 |
| container_issue | 2 |
| container_start_page | 183 |
| container_title | Test (Madrid, Spain) |
| container_volume | 22 |
| creator | Politis, Dimitris N. |
| description | The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given setup into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained. |
| doi_str_mv | 10.1007/s11749-013-0317-7 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1506358625</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2977590541</sourcerecordid><originalsourceid>FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303</originalsourceid><addsrcrecordid>eNp1kMtKxEAQRRtRcBz9AHcBN25aq9KvZCmDLxhxo-sm06kMPeQxdieCf28PcSGCq7pQ5xbFYewS4QYBzG1ENLLkgIKDQMPNEVtgoQUvcg3HKaNIG13oU3YW4w5AS53jgqmXoaaWN4Eo6-box9H326zq62wfqPZu9J-U1T6OwW-m0Q99PGcnTdVGuviZS_b-cP-2euLr18fn1d2aO5mrkWtjCAtC5xCM3mgySuaApZCCSJlSFETOSdlURFWu6lqBIUKQpUFZCRBLdj3f3YfhY6I42s5HR21b9TRM0aICLVShc5XQqz_obphCn76zKJQUAmVRJgpnyoUhxkCN3QffVeHLItiDSDuLtEmkPYi0JnXyuRMT228p_Lr8b-kbNtdz_g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1354331489</pqid></control><display><type>article</type><title>Model-free model-fitting and predictive distributions</title><source>ABI/INFORM global</source><creator>Politis, Dimitris N.</creator><creatorcontrib>Politis, Dimitris N.</creatorcontrib><description>The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given setup into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained.</description><identifier>ISSN: 1133-0686</identifier><identifier>EISSN: 1863-8260</identifier><identifier>DOI: 10.1007/s11749-013-0317-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Datasets ; Economics ; Estimates ; Finance ; Fittings ; Hypothesis testing ; Insurance ; Invited Paper ; Management ; Mathematics and Statistics ; Predictions ; Random variables ; Regression analysis ; Statistical Theory and Methods ; Statistics ; Statistics for Business ; Stochastic models</subject><ispartof>Test (Madrid, Spain), 2013-06, Vol.22 (2), p.183-221</ispartof><rights>Sociedad de Estadística e Investigación Operativa 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303</citedby><cites>FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1354331489?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,778,782,11671,27907,27908,36043,36044,44346</link.rule.ids></links><search><creatorcontrib>Politis, Dimitris N.</creatorcontrib><title>Model-free model-fitting and predictive distributions</title><title>Test (Madrid, Spain)</title><addtitle>TEST</addtitle><description>The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given setup into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained.</description><subject>Datasets</subject><subject>Economics</subject><subject>Estimates</subject><subject>Finance</subject><subject>Fittings</subject><subject>Hypothesis testing</subject><subject>Insurance</subject><subject>Invited Paper</subject><subject>Management</subject><subject>Mathematics and Statistics</subject><subject>Predictions</subject><subject>Random variables</subject><subject>Regression analysis</subject><subject>Statistical Theory and Methods</subject><subject>Statistics</subject><subject>Statistics for Business</subject><subject>Stochastic models</subject><issn>1133-0686</issn><issn>1863-8260</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kMtKxEAQRRtRcBz9AHcBN25aq9KvZCmDLxhxo-sm06kMPeQxdieCf28PcSGCq7pQ5xbFYewS4QYBzG1ENLLkgIKDQMPNEVtgoQUvcg3HKaNIG13oU3YW4w5AS53jgqmXoaaWN4Eo6-box9H326zq62wfqPZu9J-U1T6OwW-m0Q99PGcnTdVGuviZS_b-cP-2euLr18fn1d2aO5mrkWtjCAtC5xCM3mgySuaApZCCSJlSFETOSdlURFWu6lqBIUKQpUFZCRBLdj3f3YfhY6I42s5HR21b9TRM0aICLVShc5XQqz_obphCn76zKJQUAmVRJgpnyoUhxkCN3QffVeHLItiDSDuLtEmkPYi0JnXyuRMT228p_Lr8b-kbNtdz_g</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Politis, Dimitris N.</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0T</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20130601</creationdate><title>Model-free model-fitting and predictive distributions</title><author>Politis, Dimitris N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Datasets</topic><topic>Economics</topic><topic>Estimates</topic><topic>Finance</topic><topic>Fittings</topic><topic>Hypothesis testing</topic><topic>Insurance</topic><topic>Invited Paper</topic><topic>Management</topic><topic>Mathematics and Statistics</topic><topic>Predictions</topic><topic>Random variables</topic><topic>Regression analysis</topic><topic>Statistical Theory and Methods</topic><topic>Statistics</topic><topic>Statistics for Business</topic><topic>Stochastic models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Politis, Dimitris N.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI商业信息数据库</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM global</collection><collection>ProQuest Healthcare Administration Database</collection><collection>Engineering Database</collection><collection>One Business (ProQuest)</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>Engineering collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Test (Madrid, Spain)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Politis, Dimitris N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model-free model-fitting and predictive distributions</atitle><jtitle>Test (Madrid, Spain)</jtitle><stitle>TEST</stitle><date>2013-06-01</date><risdate>2013</risdate><volume>22</volume><issue>2</issue><spage>183</spage><epage>221</epage><pages>183-221</pages><issn>1133-0686</issn><eissn>1863-8260</eissn><abstract>The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given setup into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s11749-013-0317-7</doi><tpages>39</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1133-0686 |
| ispartof | Test (Madrid, Spain), 2013-06, Vol.22 (2), p.183-221 |
| issn | 1133-0686 1863-8260 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1506358625 |
| source | ABI/INFORM global |
| subjects | Datasets Economics Estimates Finance Fittings Hypothesis testing Insurance Invited Paper Management Mathematics and Statistics Predictions Random variables Regression analysis Statistical Theory and Methods Statistics Statistics for Business Stochastic models |
| title | Model-free model-fitting and predictive distributions |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T09%3A24%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Model-free%20model-fitting%20and%20predictive%20distributions&rft.jtitle=Test%20(Madrid,%20Spain)&rft.au=Politis,%20Dimitris%20N.&rft.date=2013-06-01&rft.volume=22&rft.issue=2&rft.spage=183&rft.epage=221&rft.pages=183-221&rft.issn=1133-0686&rft.eissn=1863-8260&rft_id=info:doi/10.1007/s11749-013-0317-7&rft_dat=%3Cproquest_cross%3E2977590541%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c425t-677e18e1cc1076b6e7542019343ee57938eecc44faeea25dd507ee1049714a303%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1354331489&rft_id=info:pmid/&rfr_iscdi=true |