Types of covariate and distribution effects on parameter estimates and goodness-of-fit test using clustering partitioning strategy for multinomial logistic regression / Hamzah Abdul Hamid

Abdul Hamid, Hamzah (2017) Types of covariate and distribution effects on parameter estimates and goodness-of-fit test using clustering partitioning strategy for multinomial logistic regression / Hamzah Abdul Hamid. In: The Doctoral Research Abstracts. IGS Biannual Publication, 12 (12). Institute of Graduate Studies, UiTM, Shah Alam.

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Abstract

This thesis presents a simulation study on parameter estimation for binary and multinomial logistic regression, and the extension of the clustering partitioning strategy for goodness-of-fit test to multinomial logistic regression model. The motivation behind this study is influenced by two main factors. Firstly, parameter estimation is often sensitive to sample size and types of data. Simulation studies are useful to assess and confirm the effects of parameter estimation for binary and multinomial logistic regression under various conditions. The first phase of this study covers the effect of different types of covariate, distributions and sample size on parameter estimation for binary and multinomial logistic regression model. Data were simulated for different sample sizes, types of covariate (continuous, count, categorical) and distributions (normal or skewed for continuous variable). The simulation results show that the effect of skewed and categorical covariate reduces as sample size increases. The parameter estimates for normal distribution covariate apparently are less affected by sample size. For multinomial logistic regression model with a single covariate, a sample size of at least 300 is required to obtain unbiased estimates when the covariate is positively skewed or is a categorical covariate.

Item Type: Book Section
Creators:
CreatorsEmail
Abdul Hamid, HamzahUNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IGS Biannual Publication
Volume: 12
Number: 12
Item ID: 18960
Uncontrolled Keywords: Abstract; Abstract of Thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; Multinomial logistic regression
Last Modified: 07 Jun 2018 01:45
Depositing User: Admin Pendigitan 2 PTAR
URI: http://ir.uitm.edu.my/id/eprint/18960

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