Goodness of fit for the logistic regression model using relative belief

Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration...
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Autor*in:	Al-Labadi, Luai [verfasserIn] Baskurt, Zeynep Evans, Michael

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Model checking Concentration Relative belief ratio

Anmerkung:	© The Author(s) 2017

Übergeordnetes Werk:	Enthalten in: Journal of Statistical Distributions and Applications - Heidelberg : SpringerOpen, 2014, 4(2017), 1 vom: 31. Aug.
Übergeordnetes Werk:	volume:4 ; year:2017 ; number:1 ; day:31 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s40488-017-0070-7

Katalog-ID:	SPR036524387

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allfields	10.1186/s40488-017-0070-7 doi (DE-627)SPR036524387 (SPR)s40488-017-0070-7-e DE-627 ger DE-627 rakwb eng Al-Labadi, Luai verfasserin aut Goodness of fit for the logistic regression model using relative belief 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213 Baskurt, Zeynep aut Evans, Michael (orcid)0000-0002-3899-013X aut Enthalten in Journal of Statistical Distributions and Applications Heidelberg : SpringerOpen, 2014 4(2017), 1 vom: 31. Aug. (DE-627)789478021 (DE-600)2775281-1 2195-5832 nnns volume:4 year:2017 number:1 day:31 month:08 https://dx.doi.org/10.1186/s40488-017-0070-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 31 08
spelling	10.1186/s40488-017-0070-7 doi (DE-627)SPR036524387 (SPR)s40488-017-0070-7-e DE-627 ger DE-627 rakwb eng Al-Labadi, Luai verfasserin aut Goodness of fit for the logistic regression model using relative belief 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213 Baskurt, Zeynep aut Evans, Michael (orcid)0000-0002-3899-013X aut Enthalten in Journal of Statistical Distributions and Applications Heidelberg : SpringerOpen, 2014 4(2017), 1 vom: 31. Aug. (DE-627)789478021 (DE-600)2775281-1 2195-5832 nnns volume:4 year:2017 number:1 day:31 month:08 https://dx.doi.org/10.1186/s40488-017-0070-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 31 08
allfields_unstemmed	10.1186/s40488-017-0070-7 doi (DE-627)SPR036524387 (SPR)s40488-017-0070-7-e DE-627 ger DE-627 rakwb eng Al-Labadi, Luai verfasserin aut Goodness of fit for the logistic regression model using relative belief 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213 Baskurt, Zeynep aut Evans, Michael (orcid)0000-0002-3899-013X aut Enthalten in Journal of Statistical Distributions and Applications Heidelberg : SpringerOpen, 2014 4(2017), 1 vom: 31. Aug. (DE-627)789478021 (DE-600)2775281-1 2195-5832 nnns volume:4 year:2017 number:1 day:31 month:08 https://dx.doi.org/10.1186/s40488-017-0070-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 31 08
allfieldsGer	10.1186/s40488-017-0070-7 doi (DE-627)SPR036524387 (SPR)s40488-017-0070-7-e DE-627 ger DE-627 rakwb eng Al-Labadi, Luai verfasserin aut Goodness of fit for the logistic regression model using relative belief 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213 Baskurt, Zeynep aut Evans, Michael (orcid)0000-0002-3899-013X aut Enthalten in Journal of Statistical Distributions and Applications Heidelberg : SpringerOpen, 2014 4(2017), 1 vom: 31. Aug. (DE-627)789478021 (DE-600)2775281-1 2195-5832 nnns volume:4 year:2017 number:1 day:31 month:08 https://dx.doi.org/10.1186/s40488-017-0070-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 31 08
allfieldsSound	10.1186/s40488-017-0070-7 doi (DE-627)SPR036524387 (SPR)s40488-017-0070-7-e DE-627 ger DE-627 rakwb eng Al-Labadi, Luai verfasserin aut Goodness of fit for the logistic regression model using relative belief 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213 Baskurt, Zeynep aut Evans, Michael (orcid)0000-0002-3899-013X aut Enthalten in Journal of Statistical Distributions and Applications Heidelberg : SpringerOpen, 2014 4(2017), 1 vom: 31. Aug. (DE-627)789478021 (DE-600)2775281-1 2195-5832 nnns volume:4 year:2017 number:1 day:31 month:08 https://dx.doi.org/10.1186/s40488-017-0070-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 31 08
language	English
source	Enthalten in Journal of Statistical Distributions and Applications 4(2017), 1 vom: 31. Aug. volume:4 year:2017 number:1 day:31 month:08
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author	Al-Labadi, Luai
spellingShingle	Al-Labadi, Luai misc Model checking misc Concentration misc Relative belief ratio Goodness of fit for the logistic regression model using relative belief
authorStr	Al-Labadi, Luai
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topic_title	Goodness of fit for the logistic regression model using relative belief Model checking (dpeaa)DE-He213 Concentration (dpeaa)DE-He213 Relative belief ratio (dpeaa)DE-He213
topic	misc Model checking misc Concentration misc Relative belief ratio
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title	Goodness of fit for the logistic regression model using relative belief
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title_full	Goodness of fit for the logistic regression model using relative belief
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author_browse	Al-Labadi, Luai Baskurt, Zeynep Evans, Michael
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title_sort	goodness of fit for the logistic regression model using relative belief
title_auth	Goodness of fit for the logistic regression model using relative belief
abstract	Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. © The Author(s) 2017
abstractGer	Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. © The Author(s) 2017
abstract_unstemmed	Abstract A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression. © The Author(s) 2017
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title_short	Goodness of fit for the logistic regression model using relative belief
url	https://dx.doi.org/10.1186/s40488-017-0070-7
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author2	Baskurt, Zeynep Evans, Michael
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up_date	2024-07-03T18:09:01.310Z
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