PhD Student Research
Below are some PhD research studies at the Department of Statistics:
Title & Student's details: Skewed Elliptical Distributions and their Applications: [PhD Student: Iman Al Hashmi - 47910]
Summary:
The main objectives of the proposed research are to construct a new ECD and skewed ECD, and investigate their mathematical properties as well as to use the constructed distributions in applied contexts and compare their performances with the existing results.
Title & Student's details: On multivariate classification and minimization of misclassification rates: [PhD Student: Mubarak Al Shukeili - 29026]
Summary:
The main objective of the study is to develop statistical model aimed to generate optimal separable hyperplanes that minimizes the MCR. Performance of other classification methods and techniques will be studied theoretically. A method based on the MM Principle will be developed. New classification method based on the support vector machine (SVM) will be proposed and validated theoretically and numerically using simulated and real life data.
Title & Student's details: Bias reduction in parameter estimation under missing data conditions: [PhD Student: Muna Al Shaaibi - 46933]
Summary:
The main objective of this study is propose a method of estimating parameters under data missingness condictions. The EM algorithm and MI are popular methods for dealing with missing data which show superiority over the traditional methods. Inspite of these efforts, parameters derived from these methods are still associated with high biases in the estimated parameters. Missing data present various problems. The proposed method will be validated and its performance compared with the current methods using simulation studies as well as real life data.
Title & Student's details: Parametric sub-distribution hazard model for clustered competing risks: [PhD Student: Noora Al Shanfari - 11906]
Summary:
The main objective of this study is to analyse clustered competing risk data using parametric approach. In the presence of competing, two different models can be used to analyze competing risk data, the cause-specific hazard model and the subdistribution hazard model. The latter model is used to estimate the effect of the covariates on the cumulative incidence function. However, in medical research, there are applications involving competing risks where individuals may be correlated. in this case, two models can be fit, the frailty model and the marginalized model. Marginal models have a population-averaged interpretation. Few attempts have been made for modeling clustered competing risk data using marginalized models. However, until now no attempts have been made using a parametric approach.
MSc Student Research
Title & Student's details:
Hierarchical Support Vector Machine Classifier for Diabetes Control [Nadia Al Habsi, 40448, Fall2023]
Summary:
A crucial indicator of management and medication compliance is the accurate classification
of diabetic patients based on the degree of metabolic control. Endocrinologists create and
constantly monitor set targets for individuals with diabetes in order to reduce the high-risk
effects of metabolic disorders. Nonetheless, accurate classification is preferred to determine
metabolic regulation because of changes in the normal range (4.4 -10 millimoles per liter)
before and after meals. The available metrics do not take into account the significant linked
demographic and related environmental factors, which may have an impact on how
effectively diabetic patient's metabolism is controlled. Additionally, although the support
vector machine (SVM) is one of the most popular statistical method used in classification
through determining optimal linear hyperplane, it is sometimes identified with lower levels of
classification efficiency.
This study has a two-pronged aim, that is to improve the performance of SVM by proposing
hierarchical SVM and to determine the metabolic control among patients with Type 2
Diabetes (T2D) using data from the Oman National Health NCD risk factors 2017 Survey.
BSc Student Research
Final Year Project (FYP) SPRING 2023
|
Code/Section
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Stud ID
|
Student name
|
Name of Supervisor
|
Title of project
|
|
STAT5557/30
|
123427
|
Ahmed AL-Qari
|
Dr. Khidir Abdelbasit
|
Impact Of The COVID-19 Pandemic On The Academic Performance Of Sultan Qaboos University Students
|
|
121891
|
Majdi Al-Jabri
|
|
STAT5557/20
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120560
|
Marwan Said Al-Omairi
|
Dr. Maryam Al Alawi
|
Diagnoses Related To Vitamin-D Levels And Other Factors
|
|
119882
|
Yousuf Mubarak Al-Ismaili
|
|
STAT5557/40
|
122139
|
Murshed Saif Al-Sulimani
|
Dr. Sarr Amadou
|
Low Density Lipoprotein And Blood Glucose
|
|
120865
|
Ahmed Mansoor Al-Kharousi
|
|
STAT5557/50
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120296
|
Abdul Jaleel Al-Ruzaiqi
|
Dr. Brahim Benaid
|
Analysis Of Data Of Most Common Cancers In Oman From 2009 To 2019.
|
|
118960
|
Rashid Khamis Al-Balushi
|
|
STAT5557/10
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122992
|
Rawnaq Kkalfan Al-Mahrazi
|
Dr. Moh’d Alodat
|
Investigation Of The Divorce Cases In Oman Between 2016 And 2020
|
|
123786
|
Rifaa Saud Al-Hadhrami
|
|
STAT5502/10
|
125881
|
Hind Hamed Al-Habsi
|
Prof. Islam Mazharul
|
Heteroscedasticity In Regression Analysis: Effects, Sources And Solution
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Miscellaneous Students' Research
End Of Course Projects SPRING 2023
- STAT2101 Introduction to Statistics Active Learning Collaborative Projects
