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
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...
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Institute of Graduate Studies, UiTM
2017
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| Online Access: | http://ir.uitm.edu.my/id/eprint/18960/ http://ir.uitm.edu.my/id/eprint/18960/1/ABS_HAMZAH%20ABDUL%20HAMID%20TDRA%20VOL%2012%20IGS%2017.pdf |
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eprints |
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uitm-189602018-06-07T01:45:40Z http://ir.uitm.edu.my/id/eprint/18960/ 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 Instruments and machines 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. Institute of Graduate Studies, UiTM 2017 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/18960/1/ABS_HAMZAH%20ABDUL%20HAMID%20TDRA%20VOL%2012%20IGS%2017.pdf 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. |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Teknologi MARA |
| building |
UiTM Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
Instruments and machines |
| spellingShingle |
Instruments and machines Abdul Hamid, Hamzah 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 |
| description |
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. |
| format |
Book Section |
| author |
Abdul Hamid, Hamzah |
| author_facet |
Abdul Hamid, Hamzah |
| author_sort |
Abdul Hamid, Hamzah |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| publisher |
Institute of Graduate Studies, UiTM |
| publishDate |
2017 |
| url |
http://ir.uitm.edu.my/id/eprint/18960/ http://ir.uitm.edu.my/id/eprint/18960/1/ABS_HAMZAH%20ABDUL%20HAMID%20TDRA%20VOL%2012%20IGS%2017.pdf |
| first_indexed |
2023-09-18T23:01:33Z |
| last_indexed |
2023-09-18T23:01:33Z |
| _version_ |
1777418207074713600 |