Hamzah Abdul Hamid
Thumbnail Image
Preferred name
Hamzah Abdul Hamid
Official Name
Hamzah, Abdul Hamid
Alternative Name
Hamid, Hamzah Abdul
Abdul Hamid, Hamzah
Main Affiliation
Scopus Author ID
57192434954
Researcher ID
CUF-2323-2022

Research Output

Filters

2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    The effect of different distance matrix on goodness-of-fit test for multinomial logistic regression
    (AIP Publishing, 2023)
    Hamzah Abdul Hamid
    ;
    Yap Bee Wah
    In a previous study, the performance of goodness-of-fit test based on clustering partitioning strategy for multinomial logistic regression has been investigated by using different type of clustering techniques. It is known that the clustering technique involves distance calculation to identify which cluster a data belongs to. There are several distance matrices available, but the most popular one is the Euclidean distance. Thus the previous study only considered the Euclidean distance in the process of clustering the data. In this study, the effect of different distance matrix has been investigated. Four different distance matrices, which are Maximum, Manhattan, Minkowski and Canberra were used to investigate the effect on the performance of the test. The results were then compared with the results from previous studies, which used Euclidean distance. The results show that Canberra distance produced different mean value and rejection rate while the other distance measurements produced the same mean value and rejection rate as Euclidean distance.
  • Publication
    The effect of divisive analysis clustering technique on goodness-of-fit test for multinomial logistic regression
    (Semarak Ilmu Publishing, 2025-06)
    Hamzah Abdul Hamid
    ;
    Yap Bee Wah
    ;
    Khatijahhusna Abdul Rani
    ;
    Xian Jin Xie
    The relationship between a categorical dependent variable and independent variable(s) are usually modelled using the logistic regression method. There are three types of logistic regression: binary, multinomial, and ordinal. When there is two cayegories of dependent variable, binary logistic regression is used while when there is more than two nominal categories of dependent variable, multinomial logistic regression is employed. Ordinal logistic regression is used when the dependent variable contains more than two ordinal categories. All regression models should be checked after being fitted to the data to see whether it matches the data or not. For multinomial logistic regression, there are numbers of goodness-of-fit test proposed and can be used to evaluate the fit of the model. One of the proposed tests is based on clustering partitioning strategy. Howevert, the proposed test only considered agglomerative nesting (AGNES) hierarchical clustering technique, which is Ward’s to group the data. The performance of the test using divisive analysis (DIANA) hierarchical clustering technique is remain unknown. Thus, this study attempts to examine the power of the test using divisive analysis clustering technique. Simulation technique is used to evaluate the performance of the test. The results showed that that the test using DIANA clustering technique has managed type I error and the mean are close to hypothesized values. It also has almost equivalent power with the test using Ward’s clustering technique in detecting omission of a quadratic term. However, the test using Ward’s clustering technique shows noticeably higher power in detecting omission of an interaction term.