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 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.