Loading…

Nonparametric imputation method for nonresponse in surveys

Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques...

Full description

Saved in:
Bibliographic Details
Published in:Statistical methods & applications 2020-03, Vol.29 (1), p.25-48
Main Authors: Hasler, Caren, Craiu, Radu V.
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-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473
cites cdi_FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473
container_end_page 48
container_issue 1
container_start_page 25
container_title Statistical methods & applications
container_volume 29
creator Hasler, Caren
Craiu, Radu V.
description Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.
doi_str_mv 10.1007/s10260-019-00458-w
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2376771327</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2202952465</sourcerecordid><originalsourceid>FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473</originalsourceid><addsrcrecordid>eNp9kE9LAzEQxYMoWKtfwNOC5-jk32bjTYpWoehFwVtIdxPdYpM12bX025t2BW-FgRlm3nsDP4QuCVwTAHmTCNASMBCFAbio8OYITUhJGFYVeT_ezxWmgsApOktpBcAY42yCbp-D70w0a9vHti7adTf0pm-DL_LmMzSFC7HwwUebuuCTLVpfpCH-2G06RyfOfCV78den6O3h_nX2iBcv86fZ3QLXrIIec2UEU5JWHEA0QEVT5pKWMmmUoruzFA1tBHOlWCpZC-6WRIBraisUl2yKrsbcLobvwaZer8IQfX6pc0YpJWH0sIoCVYLyUmQVHVV1DClF63QX27WJW01A70jqkaTOJPWepN5kExtNKYv9h43_0Qdcv00idTA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2202952465</pqid></control><display><type>article</type><title>Nonparametric imputation method for nonresponse in surveys</title><source>EBSCOhost Business Source Ultimate</source><source>EBSCOhost Econlit with Full Text</source><source>Springer Nature</source><creator>Hasler, Caren ; Craiu, Radu V.</creator><creatorcontrib>Hasler, Caren ; Craiu, Radu V.</creatorcontrib><description>Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.</description><identifier>ISSN: 1618-2510</identifier><identifier>EISSN: 1613-981X</identifier><identifier>DOI: 10.1007/s10260-019-00458-w</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Chemistry and Earth Sciences ; Computer Science ; Computer simulation ; Dependence ; Economics ; Finance ; Formulations ; Health Sciences ; Humanities ; Insurance ; Law ; Management ; Mathematics and Statistics ; Medicine ; Nonparametric statistics ; Original Paper ; Physics ; Spline functions ; Splines ; Statistical models ; Statistical Theory and Methods ; Statistics ; Statistics for Business ; Statistics for Engineering ; Statistics for Life Sciences ; Statistics for Social Sciences ; Variables</subject><ispartof>Statistical methods &amp; applications, 2020-03, Vol.29 (1), p.25-48</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Statistical Methods &amp; Applications is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>2019© Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473</citedby><cites>FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473</cites><orcidid>0000-0002-3963-994X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Hasler, Caren</creatorcontrib><creatorcontrib>Craiu, Radu V.</creatorcontrib><title>Nonparametric imputation method for nonresponse in surveys</title><title>Statistical methods &amp; applications</title><addtitle>Stat Methods Appl</addtitle><description>Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.</description><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Dependence</subject><subject>Economics</subject><subject>Finance</subject><subject>Formulations</subject><subject>Health Sciences</subject><subject>Humanities</subject><subject>Insurance</subject><subject>Law</subject><subject>Management</subject><subject>Mathematics and Statistics</subject><subject>Medicine</subject><subject>Nonparametric statistics</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Spline functions</subject><subject>Splines</subject><subject>Statistical models</subject><subject>Statistical Theory and Methods</subject><subject>Statistics</subject><subject>Statistics for Business</subject><subject>Statistics for Engineering</subject><subject>Statistics for Life Sciences</subject><subject>Statistics for Social Sciences</subject><subject>Variables</subject><issn>1618-2510</issn><issn>1613-981X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LAzEQxYMoWKtfwNOC5-jk32bjTYpWoehFwVtIdxPdYpM12bX025t2BW-FgRlm3nsDP4QuCVwTAHmTCNASMBCFAbio8OYITUhJGFYVeT_ezxWmgsApOktpBcAY42yCbp-D70w0a9vHti7adTf0pm-DL_LmMzSFC7HwwUebuuCTLVpfpCH-2G06RyfOfCV78den6O3h_nX2iBcv86fZ3QLXrIIec2UEU5JWHEA0QEVT5pKWMmmUoruzFA1tBHOlWCpZC-6WRIBraisUl2yKrsbcLobvwaZer8IQfX6pc0YpJWH0sIoCVYLyUmQVHVV1DClF63QX27WJW01A70jqkaTOJPWepN5kExtNKYv9h43_0Qdcv00idTA</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Hasler, Caren</creator><creator>Craiu, Radu V.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-3963-994X</orcidid></search><sort><creationdate>20200301</creationdate><title>Nonparametric imputation method for nonresponse in surveys</title><author>Hasler, Caren ; Craiu, Radu V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chemistry and Earth Sciences</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Dependence</topic><topic>Economics</topic><topic>Finance</topic><topic>Formulations</topic><topic>Health Sciences</topic><topic>Humanities</topic><topic>Insurance</topic><topic>Law</topic><topic>Management</topic><topic>Mathematics and Statistics</topic><topic>Medicine</topic><topic>Nonparametric statistics</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Spline functions</topic><topic>Splines</topic><topic>Statistical models</topic><topic>Statistical Theory and Methods</topic><topic>Statistics</topic><topic>Statistics for Business</topic><topic>Statistics for Engineering</topic><topic>Statistics for Life Sciences</topic><topic>Statistics for Social Sciences</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hasler, Caren</creatorcontrib><creatorcontrib>Craiu, Radu V.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science 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><jtitle>Statistical methods &amp; applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hasler, Caren</au><au>Craiu, Radu V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonparametric imputation method for nonresponse in surveys</atitle><jtitle>Statistical methods &amp; applications</jtitle><stitle>Stat Methods Appl</stitle><date>2020-03-01</date><risdate>2020</risdate><volume>29</volume><issue>1</issue><spage>25</spage><epage>48</epage><pages>25-48</pages><issn>1618-2510</issn><eissn>1613-981X</eissn><abstract>Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10260-019-00458-w</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-3963-994X</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 1618-2510
ispartof Statistical methods & applications, 2020-03, Vol.29 (1), p.25-48
issn 1618-2510
1613-981X
language eng
recordid cdi_proquest_journals_2376771327
source EBSCOhost Business Source Ultimate; EBSCOhost Econlit with Full Text; Springer Nature
subjects Chemistry and Earth Sciences
Computer Science
Computer simulation
Dependence
Economics
Finance
Formulations
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Nonparametric statistics
Original Paper
Physics
Spline functions
Splines
Statistical models
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Variables
title Nonparametric imputation method for nonresponse in surveys
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T18%3A22%3A51IST&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=Nonparametric%20imputation%20method%20for%20nonresponse%20in%20surveys&rft.jtitle=Statistical%20methods%20&%20applications&rft.au=Hasler,%20Caren&rft.date=2020-03-01&rft.volume=29&rft.issue=1&rft.spage=25&rft.epage=48&rft.pages=25-48&rft.issn=1618-2510&rft.eissn=1613-981X&rft_id=info:doi/10.1007/s10260-019-00458-w&rft_dat=%3Cproquest_cross%3E2202952465%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c380t-49a5397284005d025d65d67e237a99249a575d2d53f65b97c54fb150fdce59473%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2202952465&rft_id=info:pmid/&rfr_iscdi=true