The department offers programs leading to the award of Bachelor of Science in Statistics (BSc. Statistics), Master Science in Statistics (MSc. Statistics) and Doctor of Philosophy in Statistics (PhD. Statistics). It also offers a special minor in Health Statistics which is designed for students pursuing careers as Statisticians in the Ministry of Health. A general minor in Statistics is also offered to students of other majors at the College of Science.

All these programs were in place prior to the existence of the department, so a bit of history is in order. The bachelor degree in Statistics started 1993, together with degrees in Mathematics and Computer Science, under the then department of Mathematics and Computing (DOMAC). The Department of Computer Science became a separate department in September 1995, and DOMAC became the Department of Mathematics and Statistics (DOMAS). The other statistics programs that started under the DOMAS  are the minor in Health Statistics (1998), Master of Science in Statistics (1999) and PhD in Statistics (2010).

1. Fulfill the role of Sultan Qaboos University (SQU) as the reference and national house of expertise in the theory and application of Statistics,
2. Open wider the doors for interdisciplinary research of direct relevance to the Sultanate of Oman,
3. Impact and invigorate the Sultanate Qaboos University's statistics academic programs,
4. Pursue cutting edge research, as well as support it through collaboration
5. Enhance statistical consulting and community services' involvement, and
6. Inspire new ways to work together with the community,

Vision

To offer quality statistics education that produces life-long learners, creative problem-solvers, productive employees and responsible citizens.

Mission

To contribute to the overall objectives of Sultan Qaboos University through excellence in statistics education, research and service to the university community and society at large.

Statistics is the study and manipulation of data, concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data.

Statistical methods are important for addressing questions in various fields of science such as industry, public policy, medicine, and virtually every branch of human life.

Worldwide, the demand for using statistical methods has increased dramatically with the big abundance of large databases in fields like web traffic, human genome, social media and e-marketing and trading.

It is the actual need to deal with this large flux in data production that drives researchers to develop new statistical methods to answer the questions that the data poses.

Therefore, the department is keen to strengthen its academic staff so as to achieve diversity in their specializations and research interests. The department's staff also follows up on new developments and updates in the fields of data analysis and related technologies; which is reflected in the study plans in the department's programs. From the beginning, the department has been keen to ensure a statistical education of international standards, and for this purpose, it submitted an application for accreditation of the bachelor’s program in statistics from the Accreditation Agency for Study Programs in Engineering, Informatics, Natural Sciences and Mathematics (ASIIN), where the department obtained accreditation for the first period on December 7 from 2018 to September 30, 2024.

We hope that this information will be useful to the visitors of our webpage, and if you need more additional information about the department and its programs, do not hesitate to contact us at the department's e-mail. DeptofStat@squ.edu.om

Program Accreditation

The Department of Statistics is maintains high and competitive international standard in training and research. The programs of study are internationally accredited. Credit goes to the Adhoc Committee members who participated in this task: Dr. Khilid Abdelbasit (Chair), Dr. Charles Bakheit and Dr. Ronald Wesonga (Rapporteur).

Research Publications

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.

Year 2021 Journal Publications

1. Amadou Sarr, (2021). A generalized Wishart distribution: matrix variate Varma transform, Far East Journal of Theoretical Statistics 63(2), 51-83. 

2. Wesonga, R., & Abdelbasit, K. (2021). Region as a risk factor for asthma prevalence: statistical evidence from administrative data. Biostatistics & Epidemiology, 5(1), 19-29.

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.

PhD Students' Research

Below are some of the ongoing PhD research studies at the Department of Statistics:

Title:

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:

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:

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:

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 competi g 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.

Data Science Analytics Lab

Overview

The Data Science Analytics Lab (DSAL) is an initiative whose primary objective is to develop relevant statistical theory for specific data types so as to generate required knowledge to support sustainable development.

Vision

To be a leading Centre of excellence for statistical research, innovations and applications

Objectives

1. To develop statistical theory, algorithms and systems for knowledge extraction and visualization

2. To generate information on patterns, insights and predictions from diverse data for various applications

3. To develop a database of relevant statistical information for timely, evidence-based decision making for sustainable development

4. To develop collaboration across users and producers of statistical information through capacity building.

Activities

1. Collaborative statistical and applications research at national, regional and international levels

2. Capacity building in statistical knowledge and the use of statistical packages, such as R programming language

3. Support industry, policy makers with workable optimal solutions to solve local problems through attachments.

Contact

For details, collaboration or otherwise, Email [wesonga@squ.edu.om].