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A model identification and selection method for varying coefficient EV models with missing responses.

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Title: A model identification and selection method for varying coefficient EV models with missing responses.
Authors: Li, Fanqun1, Wu, Houwu1, Feng, Sanying2, Fan, Yan3, Zhao, Mingtao1, mingtao.zhao@outlook.com
Source: Statistics & Computing; Feb2026, Vol. 36 Issue 1, p1-17, 17p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 189119942
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A model identification and selection method for varying coefficient EV models with missing responses.
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  Data: <searchLink fieldCode="AU" term="%22Li%2C+Fanqun%22">Li, Fanqun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Wu%2C+Houwu%22">Wu, Houwu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Feng%2C+Sanying%22">Feng, Sanying</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Fan%2C+Yan%22">Fan, Yan</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AU" term="%22Zhao%2C+Mingtao%22">Zhao, Mingtao</searchLink><relatesTo>1</relatesTo>, <i>mingtao.zhao@outlook.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Statistics+%26+Computing%22">Statistics & Computing</searchLink>; Feb2026, Vol. 36 Issue 1, p1-17, 17p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189119942
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s11222-025-10765-w
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      – Code: eng
        Text: English
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        PageCount: 17
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      – TitleFull: A model identification and selection method for varying coefficient EV models with missing responses.
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            NameFull: Li, Fanqun
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            NameFull: Wu, Houwu
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            NameFull: Feng, Sanying
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            NameFull: Fan, Yan
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 36
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