Link misspecification in generalized linear mixed models with a random intercept for binary responses

Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu, Shun [verfasserIn] Huang, Xianzheng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Grouped response Logistic regression Reclassified response

Anmerkung:	© Sociedad de Estadística e Investigación Operativa 2018

Übergeordnetes Werk:	Enthalten in: TEST - Heidelberg [u.a.] : Springer, 1992, 28(2018), 3 vom: 03. Aug., Seite 827-843
Übergeordnetes Werk:	volume:28 ; year:2018 ; number:3 ; day:03 ; month:08 ; pages:827-843

Links:	Volltext

DOI / URN:	10.1007/s11749-018-0602-6

Katalog-ID:	SPR022694056

Internformat


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520			\|a Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression.
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912			\|a GBV_ILN_65
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912			\|a GBV_ILN_70
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912			\|a GBV_ILN_95
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912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
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912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
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912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2068
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912			\|a GBV_ILN_2108
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912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
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allfields	10.1007/s11749-018-0602-6 doi (DE-627)SPR022694056 (SPR)s11749-018-0602-6-e DE-627 ger DE-627 rakwb eng Yu, Shun verfasserin aut Link misspecification in generalized linear mixed models with a random intercept for binary responses 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedad de Estadística e Investigación Operativa 2018 Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213 Huang, Xianzheng (orcid)0000-0001-7077-0869 aut Enthalten in TEST Heidelberg [u.a.] : Springer, 1992 28(2018), 3 vom: 03. Aug., Seite 827-843 (DE-627)327576898 (DE-600)2044657-3 1863-8260 nnns volume:28 year:2018 number:3 day:03 month:08 pages:827-843 https://dx.doi.org/10.1007/s11749-018-0602-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2018 3 03 08 827-843
spelling	10.1007/s11749-018-0602-6 doi (DE-627)SPR022694056 (SPR)s11749-018-0602-6-e DE-627 ger DE-627 rakwb eng Yu, Shun verfasserin aut Link misspecification in generalized linear mixed models with a random intercept for binary responses 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedad de Estadística e Investigación Operativa 2018 Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213 Huang, Xianzheng (orcid)0000-0001-7077-0869 aut Enthalten in TEST Heidelberg [u.a.] : Springer, 1992 28(2018), 3 vom: 03. Aug., Seite 827-843 (DE-627)327576898 (DE-600)2044657-3 1863-8260 nnns volume:28 year:2018 number:3 day:03 month:08 pages:827-843 https://dx.doi.org/10.1007/s11749-018-0602-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2018 3 03 08 827-843
allfields_unstemmed	10.1007/s11749-018-0602-6 doi (DE-627)SPR022694056 (SPR)s11749-018-0602-6-e DE-627 ger DE-627 rakwb eng Yu, Shun verfasserin aut Link misspecification in generalized linear mixed models with a random intercept for binary responses 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedad de Estadística e Investigación Operativa 2018 Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213 Huang, Xianzheng (orcid)0000-0001-7077-0869 aut Enthalten in TEST Heidelberg [u.a.] : Springer, 1992 28(2018), 3 vom: 03. Aug., Seite 827-843 (DE-627)327576898 (DE-600)2044657-3 1863-8260 nnns volume:28 year:2018 number:3 day:03 month:08 pages:827-843 https://dx.doi.org/10.1007/s11749-018-0602-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2018 3 03 08 827-843
allfieldsGer	10.1007/s11749-018-0602-6 doi (DE-627)SPR022694056 (SPR)s11749-018-0602-6-e DE-627 ger DE-627 rakwb eng Yu, Shun verfasserin aut Link misspecification in generalized linear mixed models with a random intercept for binary responses 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedad de Estadística e Investigación Operativa 2018 Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213 Huang, Xianzheng (orcid)0000-0001-7077-0869 aut Enthalten in TEST Heidelberg [u.a.] : Springer, 1992 28(2018), 3 vom: 03. Aug., Seite 827-843 (DE-627)327576898 (DE-600)2044657-3 1863-8260 nnns volume:28 year:2018 number:3 day:03 month:08 pages:827-843 https://dx.doi.org/10.1007/s11749-018-0602-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2018 3 03 08 827-843
allfieldsSound	10.1007/s11749-018-0602-6 doi (DE-627)SPR022694056 (SPR)s11749-018-0602-6-e DE-627 ger DE-627 rakwb eng Yu, Shun verfasserin aut Link misspecification in generalized linear mixed models with a random intercept for binary responses 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sociedad de Estadística e Investigación Operativa 2018 Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213 Huang, Xianzheng (orcid)0000-0001-7077-0869 aut Enthalten in TEST Heidelberg [u.a.] : Springer, 1992 28(2018), 3 vom: 03. Aug., Seite 827-843 (DE-627)327576898 (DE-600)2044657-3 1863-8260 nnns volume:28 year:2018 number:3 day:03 month:08 pages:827-843 https://dx.doi.org/10.1007/s11749-018-0602-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2018 3 03 08 827-843
language	English
source	Enthalten in TEST 28(2018), 3 vom: 03. Aug., Seite 827-843 volume:28 year:2018 number:3 day:03 month:08 pages:827-843
sourceStr	Enthalten in TEST 28(2018), 3 vom: 03. Aug., Seite 827-843 volume:28 year:2018 number:3 day:03 month:08 pages:827-843
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Grouped response Logistic regression Reclassified response
isfreeaccess_bool	false
container_title	TEST
authorswithroles_txt_mv	Yu, Shun @@aut@@ Huang, Xianzheng @@aut@@
publishDateDaySort_date	2018-08-03T00:00:00Z
hierarchy_top_id	327576898
id	SPR022694056
language_de	englisch
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author	Yu, Shun
spellingShingle	Yu, Shun misc Grouped response misc Logistic regression misc Reclassified response Link misspecification in generalized linear mixed models with a random intercept for binary responses
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topic_title	Link misspecification in generalized linear mixed models with a random intercept for binary responses Grouped response (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Reclassified response (dpeaa)DE-He213
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title	Link misspecification in generalized linear mixed models with a random intercept for binary responses
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title_full	Link misspecification in generalized linear mixed models with a random intercept for binary responses
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title_sort	link misspecification in generalized linear mixed models with a random intercept for binary responses
title_auth	Link misspecification in generalized linear mixed models with a random intercept for binary responses
abstract	Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. © Sociedad de Estadística e Investigación Operativa 2018
abstractGer	Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. © Sociedad de Estadística e Investigación Operativa 2018
abstract_unstemmed	Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. These tests are applied to a data set from a longitudinal study that was analyzed in the existing literature using logistic regression. © Sociedad de Estadística e Investigación Operativa 2018
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title_short	Link misspecification in generalized linear mixed models with a random intercept for binary responses
url	https://dx.doi.org/10.1007/s11749-018-0602-6
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up_date	2024-07-03T14:17:21.392Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR022694056</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230330073257.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11749-018-0602-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR022694056</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11749-018-0602-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Shun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Link misspecification in generalized linear mixed models with a random intercept for binary responses</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Sociedad de Estadística e Investigación Operativa 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We present in this paper tests for link misspecification in generalized linear mixed models with a random intercept for binary responses. To facilitate model diagnosis, we consider two types of grouped responses induced from the original responses and also stochastically create reclassified binary responses. Maximum likelihood estimators based on the original observed data and the counterpart estimators based on different induced data sets are investigated in the presence of link misspecification. Results from this investigation motivate four diagnostic tests for assessing the adequacy of an assumed link, which can provide information on how the true link differs from an assumed symmetric link when the proposed tests reject the assumed link. The performance of these tests is illustrated via simulation studies in comparison with an existing method for checking link assumptions. 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Link misspecification in generalized linear mixed models with a random intercept for binary responses

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