Pembangunan model regresi poisson sifar-melambung berintegrasi dalam biostatistik
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Date
2018-08
Authors
Zafakali, Nur Syabiha
Journal Title
Journal ISSN
Volume Title
Publisher
Pusat Pengajian Sains Perubatan, Universiti Sains Malaysia
Abstract
The Poisson regression method is an approach often chosen by researchers in
making analysis of data in the form of numbers. However, the excess zero often applies
to such data. This is due to the existence of overdispersion of the data collected. This
phenomenon led to the Poisson regression model is no longer suitable to be applied.
Alternatively, the method of Zero-Inflated Poisson regression was selected to model
the data that have excess zero phenomenon (overdispersion). This research also
emphasizes the development methodology of data analysis. The data gathered
involved two major cases studies of data from patients with Thalassemia among
children and the patients with dental caries problems. The first phase in this research
is to refer to the algorithm development procedure to model the Zero-Inflated Poisson
Regression method through the bootstrap method and combined with the fuzzy
regression method. The combination of these methods is referred to as the Integrated
Model. The second phase is the comparison of the findings between the Integrated
Model and the existing method. An overview of the overall model was also performed
to obtain information related to the efficiency of the model. The algorithm was
developed based on the concept of improvement and every detail of the methods used
will be explained carefully on the methodology. The main outcome of this research is
to refer to the development of a research methodology that also helps researchers to
analyse data more effectively and to give more accurate results. The use of the
Integrated Model of the two case studies has resulted in a more efficient average value
compared to the existing method. It shows that this method has demonstrated a better
model for each set of data that can be studied and applied successfully.
Description
Keywords
Biostatistics