Investigating the power of goodness-of-fit tests for multinomial logistic regression
Goodness-of-fit tests are important to assess if the model fits the data. In this paper we investigate the Type I error and power of two goodness-of-fit tests for multinomial logistic regression via a simulation study. The GoF test using partitioning strategy (clustering) in the covariate space, (Fo...
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my.um.eprints.227422019-10-14T08:06:51Z http://eprints.um.edu.my/22742/ Investigating the power of goodness-of-fit tests for multinomial logistic regression Abdul Hamid, Hamzah Yap, Bee Wah Xie, Xian-Jin Ong, Seng Huat QA Mathematics Goodness-of-fit tests are important to assess if the model fits the data. In this paper we investigate the Type I error and power of two goodness-of-fit tests for multinomial logistic regression via a simulation study. The GoF test using partitioning strategy (clustering) in the covariate space, (Formula presented.) was compared with another test, Cg which was based on grouping of predicted probabilities. The power of both tests was investigated when the quadratic term or an interaction term were omitted from the model. The proposed test (Formula presented.) shows good Type I error and ample power except for models with highly skewed covariate distribution. The proposed test (Formula presented.) also has good power in detecting omission of continuous interaction term.The application on a real dataset was performed to illustrate the use of goodness-of-fit test for multinomial logistic regression in practice using R. Taylor & Francis 2018 Article PeerReviewed Abdul Hamid, Hamzah and Yap, Bee Wah and Xie, Xian-Jin and Ong, Seng Huat (2018) Investigating the power of goodness-of-fit tests for multinomial logistic regression. Communications in Statistics - Simulation and Computation, 47 (4). pp. 1039-1055. ISSN 0361-0918 https://doi.org/10.1080/03610918.2017.1303727 doi:10.1080/03610918.2017.1303727 |
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QA Mathematics Abdul Hamid, Hamzah Yap, Bee Wah Xie, Xian-Jin Ong, Seng Huat Investigating the power of goodness-of-fit tests for multinomial logistic regression |
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Goodness-of-fit tests are important to assess if the model fits the data. In this paper we investigate the Type I error and power of two goodness-of-fit tests for multinomial logistic regression via a simulation study. The GoF test using partitioning strategy (clustering) in the covariate space, (Formula presented.) was compared with another test, Cg which was based on grouping of predicted probabilities. The power of both tests was investigated when the quadratic term or an interaction term were omitted from the model. The proposed test (Formula presented.) shows good Type I error and ample power except for models with highly skewed covariate distribution. The proposed test (Formula presented.) also has good power in detecting omission of continuous interaction term.The application on a real dataset was performed to illustrate the use of goodness-of-fit test for multinomial logistic regression in practice using R. |
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Article |
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Abdul Hamid, Hamzah Yap, Bee Wah Xie, Xian-Jin Ong, Seng Huat |
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Abdul Hamid, Hamzah Yap, Bee Wah Xie, Xian-Jin Ong, Seng Huat |
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Abdul Hamid, Hamzah |
title |
Investigating the power of goodness-of-fit tests for multinomial logistic regression |
title_short |
Investigating the power of goodness-of-fit tests for multinomial logistic regression |
title_full |
Investigating the power of goodness-of-fit tests for multinomial logistic regression |
title_fullStr |
Investigating the power of goodness-of-fit tests for multinomial logistic regression |
title_full_unstemmed |
Investigating the power of goodness-of-fit tests for multinomial logistic regression |
title_sort |
investigating the power of goodness-of-fit tests for multinomial logistic regression |
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Taylor & Francis |
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2018 |
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http://eprints.um.edu.my/22742/ https://doi.org/10.1080/03610918.2017.1303727 |
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