Journal article
Investigating the power of goodness-of-fit tests for multinomial logistic regression
Communications in statistics. Simulation and computation, Vol.47(4), pp.1039-1055
04/21/2018
DOI: 10.1080/03610918.2017.1303727
Abstract
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,
was compared with another test, C
g
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
shows good Type I error and ample power except for models with highly skewed covariate distribution. The proposed test
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.
Details
- Title: Subtitle
- Investigating the power of goodness-of-fit tests for multinomial logistic regression
- Creators
- Hamzah Abdul Hamid - Universiti Malaysia PerlisYap Bee Wah - Universiti Teknologi MARAXian-Jin Xie - The University of Texas Southwestern Medical CenterOng Seng Huat - University of Malaya
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Simulation and computation, Vol.47(4), pp.1039-1055
- Publisher
- Taylor & Francis
- DOI
- 10.1080/03610918.2017.1303727
- ISSN
- 0361-0918
- eISSN
- 1532-4141
- Language
- English
- Date published
- 04/21/2018
- Academic Unit
- Preventive and Community Dentistry; Biostatistics; Dental Research
- Record Identifier
- 9984367737002771
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