Loading…
Bootstrap Approximations in Model Checks for Regression
Let M = m θ θ θ be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H 0 : m ε M), it is known that optimal tests should be based on the em...
Saved in:
| Published in: | Journal of the American Statistical Association 1998-03, Vol.93 (441), p.141-149 |
|---|---|
| Main Authors: | , , |
| 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-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093 |
| container_end_page | 149 |
| container_issue | 441 |
| container_start_page | 141 |
| container_title | Journal of the American Statistical Association |
| container_volume | 93 |
| creator | Stute, W. Manteiga, W. González Quindimil, M. Presedo |
| description | Let M = m
θ
θ θ be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H
0
: m ε M), it is known that optimal tests should be based on the empirical process of the regressors marked by the residuals. In this article we show that the distribution of this process may be approximated by the wild bootstrap. The method is applied to simulated datasets as well as to real data. |
| doi_str_mv | 10.1080/01621459.1998.10474096 |
| format | article |
| fullrecord | <record><control><sourceid>jstor_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_01621459_1998_10474096</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>2669611</jstor_id><sourcerecordid>2669611</sourcerecordid><originalsourceid>FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093</originalsourceid><addsrcrecordid>eNqFkF1LHDEUhkOx0NX6F8qgpXdj8_1xuS61LSiCKHgXMpOkznZ2subMYv33Zli3lYKYm0DynDdvHoQ-EXxCsMZfMZGUcGFOiDG6HHHFsZHv0IwIpmqq-O0emk1QPVEf0D7AEpeltJ4hdZrSCGN262q-Xuf0p1u5sUsDVN1QXSQf-mpxF9rfUMWUq6vwKweAcv8RvY-uh3D4vB-gm7Nv14sf9fnl95-L-XndckzGWnvdeMNN9L5pqNCYaKyEaXhUPHAflSK0DR5Lql0jpNTC4-gaFT3RrnyDHaAv29zS7X4TYLSrDtrQ924IaQOWaaG55KKAR_-By7TJQ-lmiwIpJGe4QMevQaRoYwxLNr0pt1SbE0AO0a5z0ZIfLcF2Um53yu2k3O6Ul8HPz_EOWtfH7Ia2g7_TlEpFufmHLWFM-WU4ZVhZKqWRhBRsvsW6obhfuYeUe29H99invItmbzR6AsH7nlo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>274656430</pqid></control><display><type>article</type><title>Bootstrap Approximations in Model Checks for Regression</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>JSTOR Archival Journals and Primary Sources Collection</source><source>ABI/INFORM Global</source><source>Taylor and Francis Science and Technology Collection</source><creator>Stute, W. ; Manteiga, W. González ; Quindimil, M. Presedo</creator><creatorcontrib>Stute, W. ; Manteiga, W. González ; Quindimil, M. Presedo</creatorcontrib><description>Let M = m
θ
θ θ be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H
0
: m ε M), it is known that optimal tests should be based on the empirical process of the regressors marked by the residuals. In this article we show that the distribution of this process may be approximated by the wild bootstrap. The method is applied to simulated datasets as well as to real data.</description><identifier>ISSN: 0162-1459</identifier><identifier>EISSN: 1537-274X</identifier><identifier>DOI: 10.1080/01621459.1998.10474096</identifier><identifier>CODEN: JSTNAL</identifier><language>eng</language><publisher>Alexandria, VA: Taylor & Francis Group</publisher><subject>Approximation ; Critical values ; Estimators ; Exact sciences and technology ; Goodness of fit ; Inference from stochastic processes; time series analysis ; Linear inference, regression ; Linear models ; Linear regression ; Marked empirical process ; Mathematical functions ; Mathematical models ; Mathematics ; P values ; Parametric models ; Polynomials ; Probability and statistics ; Regression analysis ; Residuals ; Sciences and techniques of general use ; Statistical methods ; Statistical models ; Statistics ; Theory and Methods ; Wild bootstrap</subject><ispartof>Journal of the American Statistical Association, 1998-03, Vol.93 (441), p.141-149</ispartof><rights>Copyright Taylor & Francis Group, LLC 1998</rights><rights>Copyright 1998 American Statistical Association</rights><rights>1998 INIST-CNRS</rights><rights>Copyright American Statistical Association Mar 1998</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093</citedby><cites>FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2669611$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/274656430?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,12847,27924,27925,33223,33224,36060,36061,44363,58238,58471</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2267249$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Stute, W.</creatorcontrib><creatorcontrib>Manteiga, W. González</creatorcontrib><creatorcontrib>Quindimil, M. Presedo</creatorcontrib><title>Bootstrap Approximations in Model Checks for Regression</title><title>Journal of the American Statistical Association</title><description>Let M = m
θ
θ θ be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H
0
: m ε M), it is known that optimal tests should be based on the empirical process of the regressors marked by the residuals. In this article we show that the distribution of this process may be approximated by the wild bootstrap. The method is applied to simulated datasets as well as to real data.</description><subject>Approximation</subject><subject>Critical values</subject><subject>Estimators</subject><subject>Exact sciences and technology</subject><subject>Goodness of fit</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Linear inference, regression</subject><subject>Linear models</subject><subject>Linear regression</subject><subject>Marked empirical process</subject><subject>Mathematical functions</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>P values</subject><subject>Parametric models</subject><subject>Polynomials</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Residuals</subject><subject>Sciences and techniques of general use</subject><subject>Statistical methods</subject><subject>Statistical models</subject><subject>Statistics</subject><subject>Theory and Methods</subject><subject>Wild bootstrap</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNqFkF1LHDEUhkOx0NX6F8qgpXdj8_1xuS61LSiCKHgXMpOkznZ2subMYv33Zli3lYKYm0DynDdvHoQ-EXxCsMZfMZGUcGFOiDG6HHHFsZHv0IwIpmqq-O0emk1QPVEf0D7AEpeltJ4hdZrSCGN262q-Xuf0p1u5sUsDVN1QXSQf-mpxF9rfUMWUq6vwKweAcv8RvY-uh3D4vB-gm7Nv14sf9fnl95-L-XndckzGWnvdeMNN9L5pqNCYaKyEaXhUPHAflSK0DR5Lql0jpNTC4-gaFT3RrnyDHaAv29zS7X4TYLSrDtrQ924IaQOWaaG55KKAR_-By7TJQ-lmiwIpJGe4QMevQaRoYwxLNr0pt1SbE0AO0a5z0ZIfLcF2Um53yu2k3O6Ul8HPz_EOWtfH7Ia2g7_TlEpFufmHLWFM-WU4ZVhZKqWRhBRsvsW6obhfuYeUe29H99invItmbzR6AsH7nlo</recordid><startdate>19980301</startdate><enddate>19980301</enddate><creator>Stute, W.</creator><creator>Manteiga, W. González</creator><creator>Quindimil, M. Presedo</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>JRZRW</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</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>19980301</creationdate><title>Bootstrap Approximations in Model Checks for Regression</title><author>Stute, W. ; Manteiga, W. González ; Quindimil, M. Presedo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Approximation</topic><topic>Critical values</topic><topic>Estimators</topic><topic>Exact sciences and technology</topic><topic>Goodness of fit</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Linear inference, regression</topic><topic>Linear models</topic><topic>Linear regression</topic><topic>Marked empirical process</topic><topic>Mathematical functions</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>P values</topic><topic>Parametric models</topic><topic>Polynomials</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Residuals</topic><topic>Sciences and techniques of general use</topic><topic>Statistical methods</topic><topic>Statistical models</topic><topic>Statistics</topic><topic>Theory and Methods</topic><topic>Wild bootstrap</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stute, W.</creatorcontrib><creatorcontrib>Manteiga, W. González</creatorcontrib><creatorcontrib>Quindimil, M. Presedo</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 35</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - West</collection><collection>Primary Sources Access (Plan D) - International</collection><collection>Primary Sources Access & Build (Plan A) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Midwest</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Northeast</collection><collection>Primary Sources Access (Plan D) - Southeast</collection><collection>Primary Sources Access (Plan D) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Southeast</collection><collection>Primary Sources Access (Plan D) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - UK / I</collection><collection>Primary Sources Access (Plan D) - Canada</collection><collection>Primary Sources Access (Plan D) - EMEALA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - International</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - International</collection><collection>Primary Sources Access (Plan D) - West</collection><collection>Periodicals Index Online Segments 1-50</collection><collection>Primary Sources Access (Plan D) - APAC</collection><collection>Primary Sources Access (Plan D) - Midwest</collection><collection>Primary Sources Access (Plan D) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Canada</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - EMEALA</collection><collection>Primary Sources Access & Build (Plan A) - APAC</collection><collection>Primary Sources Access & Build (Plan A) - Canada</collection><collection>Primary Sources Access & Build (Plan A) - West</collection><collection>Primary Sources Access & Build (Plan A) - EMEALA</collection><collection>Primary Sources Access (Plan D) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - Midwest</collection><collection>Primary Sources Access & Build (Plan A) - North Central</collection><collection>Primary Sources Access & Build (Plan A) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - Southeast</collection><collection>Primary Sources Access (Plan D) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - APAC</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - MEA</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest - Health & Medical Complete保健、医学与药学数据库</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>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>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Healthcare Administration Database</collection><collection>PML(ProQuest Medical Library)</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>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>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>Stute, W.</au><au>Manteiga, W. González</au><au>Quindimil, M. Presedo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bootstrap Approximations in Model Checks for Regression</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1998-03-01</date><risdate>1998</risdate><volume>93</volume><issue>441</issue><spage>141</spage><epage>149</epage><pages>141-149</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><coden>JSTNAL</coden><abstract>Let M = m
θ
θ θ be a parametric model for an unknown regression function m. For example, M may consist of all polynomials or trigonometric polynomials with a given bound on the degree. To check the full model M (i.e., to test for H
0
: m ε M), it is known that optimal tests should be based on the empirical process of the regressors marked by the residuals. In this article we show that the distribution of this process may be approximated by the wild bootstrap. The method is applied to simulated datasets as well as to real data.</abstract><cop>Alexandria, VA</cop><pub>Taylor & Francis Group</pub><doi>10.1080/01621459.1998.10474096</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0162-1459 |
| ispartof | Journal of the American Statistical Association, 1998-03, Vol.93 (441), p.141-149 |
| issn | 0162-1459 1537-274X |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_01621459_1998_10474096 |
| source | International Bibliography of the Social Sciences (IBSS); JSTOR Archival Journals and Primary Sources Collection; ABI/INFORM Global; Taylor and Francis Science and Technology Collection |
| subjects | Approximation Critical values Estimators Exact sciences and technology Goodness of fit Inference from stochastic processes time series analysis Linear inference, regression Linear models Linear regression Marked empirical process Mathematical functions Mathematical models Mathematics P values Parametric models Polynomials Probability and statistics Regression analysis Residuals Sciences and techniques of general use Statistical methods Statistical models Statistics Theory and Methods Wild bootstrap |
| title | Bootstrap Approximations in Model Checks for Regression |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T09%3A36%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Bootstrap%20Approximations%20in%20Model%20Checks%20for%20Regression&rft.jtitle=Journal%20of%20the%20American%20Statistical%20Association&rft.au=Stute,%20W.&rft.date=1998-03-01&rft.volume=93&rft.issue=441&rft.spage=141&rft.epage=149&rft.pages=141-149&rft.issn=0162-1459&rft.eissn=1537-274X&rft.coden=JSTNAL&rft_id=info:doi/10.1080/01621459.1998.10474096&rft_dat=%3Cjstor_cross%3E2669611%3C/jstor_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c401t-8d8bd949fddbb2580180759b4f74e4df7712ced0628ab56685d0fab7fd18a4093%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=274656430&rft_id=info:pmid/&rft_jstor_id=2669611&rfr_iscdi=true |