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Research Publications

Year 2023 Journal Publications

  1. Al-Shukeili, M., & Wesonga, R. (2023). Modifying linearly non-separable support vector machine binary classifier to account for the centroid mean vector. Communications for Statistical Applications and Methods, 30(3), 245-258.
  2. Alodat, M. D., & Baklizi, A. (2023). Pak. J. Statist. 2023 Vol. 39 (1), 11-28 Estimation of the Location-Scale Parameters Based on Ranked Set Sampling Combined with Additional Binomial Information. Pak. J. Statist, 39(1), 11-28.
  3. Islam, M. M., Wesonga, R., Al Hasani, I., & Al Manei, A. (2023). Knowledge and Attitude Towards COVID-19 and Associated Mental Health Status among Students of Sultan Qaboos University, Oman: Knowledge and attitude towards COVID-19 among students. Sultan Qaboos University Journal for Science [SQUJS], 28(2), 56-73.
  4. Arfi, M. (2023). On the Regression Estimation from Mixing Samples. Far East Journal of Theoretical Statistics, 67(1), 1–14.
  5. Pathak, A., Kumar, M., Singh, S. K., Singh, U., Tiwari, M. K., & Kumar, S. (2023). E-Bayesian inference for xgamma distribution under progressive type II censoring with binomial removals and their applications. International Journal of Modelling and Simulation, 1-20


Year 2022 Journal Publications

  1. Abdelbasit, K., & Wesonga, R. (2022). A data analytic model to determine regional variation of asthma incidence and other chronic obstructive lung diseases in Oman. Healthcare Analytics, 2, 100074.
  2. Afra Al Manei, Iman Al Hasani and Ronald Wesonga, (2022)Investigating term weighting schemes on the classification performance for the imbalanced text data, Advances and Applications in Statistics 78, 63-82.
  3. Wesonga, R., Bakheit, C., & Ababneh, F. (2022). Cluster modelling of longitudinal disease data: asthma and potential clinical phenotypes. International Journal of Modelling and Simulation, 42(2), 227-239.
  4. Islam, M. M., Wesonga, R., Al Hasani, I., & Al Manei, A. (2022). Prevalence and determinants of mental health issues among university students during COVID-19 pandemic in Oman: An Online Cross-sectional Study. International Research Journal of Public and Environmental Health, 9 (2), 43-54.
  5. Qananwah, Q., Alqudah, A. M., Alodat, M. D., Dagamseh, A., & Hayden, O. (2022). Detecting Cognitive Features of Videos Using EEG Signal. The Computer Journal, 65(1), 105-123.
  6. Pathak, A., Kumar, M., Singh, S. K., Singh, U., Tiwari, M. K., & Kumar, S. (2022). Bayesian inference for Maxwell Boltzmann distribution on step-stress partially accelerated life test under progressive type-II censoring with binomial removals. International Journal of System Assurance Engineering and Management, 13(4), 1976-2010.

  7. Alodat, M. T., Fayez, M. D. A., & Eidous, O. (2022). On the asymptotic distribution of Matusita's overlapping measure. Communications in Statistics-Theory and Methods, 51(20), 6963-6977.


Year 2021 Journal Publications

  1. Wesonga, R., & Abdelbasit, K. (2021). Region as a risk factor for asthma prevalence: statistical evidence from administrative data. Biostatistics & Epidemiology, 5(1), 19-29.
  2. Amadou Sarr, (2021). A generalized Wishart distribution: matrix variate Varma transform, Far East Journal of Theoretical Statistics 63(2), 51-83.
  3. Al-Hasani, Iman, S.S. (2021). Estimating Eectiveness of Online Geographically-based Advertising Campaigns, Durham theses, Durham University. 
  4. Al Alawi, M. (2021). Spectral clustering and downsampling-based model selection for functional data (Doctoral dissertation, University of Glasgow).
  5. Al Alawi, M., Ray, S., & Gupta, M. A New Functional Data Clustering Technique Based on Spectral Clustering and Downsampling. In Book of Abstracts (p. 103).
  6. Al Shaaibi, M., & Wesonga, R. (2021). Bias dynamics for parameter estimation with missing data mechanisms under logistic model. Journal of Statistics and Management Systems, 24(4), 873-894.
  7. Al-Shukeili, M., & Wesonga, R. (2021). A Novel Minimization Approximation Cost Classification Method to Minimize Misclassification Rate for Dichotomous and Homogeneous Classes. RMS: Research in Mathematics & Statistics, 8(1), 2021627.
  8. Al Hashmi, I. and Sarr, A (2021). Lomax-Pearson VII distribution with application to financial stock returns. Advances and Applications in Statistics, 71(2), 123-140.
  9. Okiring, J., Routledge, I., Epstein, A., Namuganga, J. F., Kamya, E. V., Obeng-Amoako, G. O., ... Wesonga, R. & Nankabirwa, J. I. (2021). Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis. BMC public health, 21(1), 1-11.
  10. Bbosa, F. F., Nabukenya, J., Nabende, P., & Wesonga, R. (2021). On the goodness of fit of parametric and non-parametric data mining techniques: the case of malaria incidence thresholds in Uganda. Health and Technology, 11(4), 929-940.
  11. K.K Shukla, Rama Shanker, Manoj Kumar & Leonida, T.E. (2021). Generalization of size – biased Poisson Sujatha distribution and its applications, Journal of Math. Computational Science, Vol. 6 (Issue 11), pp 6811 - 6828, ISSN: 1927 - 5307
  12. Kamlesh K. Shukla, Rama Shanker and Manoj Kumar (2021). A New One Parameter Discrete Distribution and its applications. Journal of Statistics and Management Systems, Vol. 25 (Issue 1), pp. 269 – 283



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]


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]


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]


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]


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]


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




Stud ID


Student name

Name of Supervisor

Title of project




Ahmed AL-Qari


Dr. Khidir Abdelbasit

Impact Of The COVID-19 Pandemic On The Academic Performance Of Sultan Qaboos University Students


Majdi Al-Jabri




Marwan Said Al-Omairi


Dr. Maryam Al Alawi

Diagnoses Related To Vitamin-D Levels And Other Factors


Yousuf Mubarak Al-Ismaili




Murshed Saif Al-Sulimani


Dr.  Sarr Amadou

Low Density Lipoprotein And Blood Glucose


Ahmed Mansoor Al-Kharousi




Abdul Jaleel Al-Ruzaiqi


Dr. Brahim Benaid

Analysis Of Data Of Most Common Cancers In Oman From 2009 To 2019.


Rashid Khamis Al-Balushi




Rawnaq Kkalfan Al-Mahrazi


Dr. Moh’d Alodat

Investigation Of The Divorce Cases In Oman Between 2016 And 2020


Rifaa Saud Al-Hadhrami



Hind Hamed Al-Habsi

Prof. Islam Mazharul

Heteroscedasticity In Regression Analysis: Effects, Sources And Solution






























Miscellaneous Students' Research

End Of Course Projects SPRING 2023

  1. STAT2101 Introduction to Statistics Active Learning Collaborative Projects









Statistics Students' Group Activities

The statistics group (STATGROUP) is a very active association of students offering statistics at the university. The STATGROUP at the Department of Statistics is dynamic and is involved in many activities.

STATGROUP held the first Statistical Forum from 27 February  to  1 March 2023.  The forum was inaugurated by Dr. Talal Al-Hosni, the Dean, College of Science who was accompanied by the HoD, Department of Statistics.


The STAT Forum targeted students from universities, colleges and schools. They focused on introducing the basic concepts of data collection and analysis with participation of four statistical consulting companies: Data mining, Muscat statistical consulting, Bahthy platform and Alfikar statistical consulting.


Click the University News Article link to access the article.


The STATGROUP with the help of the data mining company collected visitors' opinions about the forum and they received about 196 responses. They received positive feedback about the forum decorations and the valuable statistical information that they introduced and shared with the public. The availability of the statistical companies attracted visitors as well  and broadened knowledge about statistics. The students also appeared on different news media.




Click for some Radio Interviews by Hind Al Habsi and AbdulHakeem Al Abdali


The students designed a short video about the history of the statistics and how it developed over the years. The video has been requested by one of the schools who visited the forum to be shared with their students. The video can be shared with the public through the college events screen, the departmental website and social media.



Click to access a Video