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Ngailo

Name
Edward Kanuti Ngailo

Academic Rank

Department
Physics, Mathematics & Informatics(PMI)

Biography

Biography

Dr. Edward Kanuti Ngailo is a Lecturer in the Department of Physics, Mathematics and Informatics within the Faculty of Science at DUCE (Dar es Salaam University College of Education) a constituent college of the University of Dar es Salaam (UDSM), Tanzania. He holds a PhD in Mathematical Statistics from Linköping University (Sweden), a Licentiate Degree in Mathematical Statistics from Stockholm University (Sweden), and specializes in multivariate statistical analysis. His educational background includes an MSc in Mathematical Modeling and a BEd. Sc. all from the University of Dar es Salaam, Tanzania. His teaching areas encompass Mathematical Statistics, Linear Models, and Machine learning courses. His research focuses on statistical methodologies for classification, repeated measurements, multilevel models with application to educational data, and data-driven applications in energy systems and public health.

Contacts

Email:

Email Address
edward.ngailo@duce.ac.tz

Mob:

Research Interest

Research Interest
Experimental designs, Multivariate Statistical methods, Machine Learning (regression/classification), Mathematical Statistics, Linear Models, Statistical Methods for Development Evaluation, Energy Theft Detection.

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Projects

Projects
  1. The Effect of Construction of Word Problems on Student’s Performance in Primary Schools(2023–Completed, Principal Investigator). Funding: UDSM Competitive Research and Innovation Grants 2022. Collaborators: Dr. Janeth Kalinga, Dr. Fanuel Lemma (Completed)

  2. Data Driven Analysis for Energy Theft Detection in Power Grids(2022.    Co-PI)

     Collaborators: Dr. Elimboto Yohana, and Dr. Diana Rwegasira. Funding: UDSM Competitive Research and Innovation Grants 2022. On going                                                                                                                                         

Publications

Publications

1.Enhancing energy consumption modeling with a shifted-mode Lomax regression for non-zero peaks

E Ngailo, D Rwegasira, EM Elimboto Yohana

Journal of Electrical Systems and Information Technology 12, 1-26

2.Modeling and forecasting wholesale gasoline prices in Tanzania using ARIMA and neural network autoregressive models;TD Sagamiko, EK Ngailo,Tanzania Journal of Science 51 (4), 14

3.Time series analysis of energy usage patterns by Tanzania large power users;D Rwegasira, E Ngailo, E Yohana, N Masasi

Journal of Electrical Systems and Information Technology 12 (1), 60

4.Mathematical modeling of awareness-driven interventions for gender-based violence control in a closed population

FM Chuma, EK Ngailo, ZS Mussa; Tanzania Journal of Science 51 (4), 12

5. Chuma, F.M., & Ngailo, E.K.(2024). Mathematical analysis of Campylobacteriosis disease model in human with saturated incidence rate and treatment. 

6. Ngailo, E.K., & Nadarajah, S.(2023). Classification of Repeated measurements using bias corrected Euclidean distance function. Journal of the Korean Statistical Society. https://doi.org/10.1007/s42952-023-00246-z

7. Ngailo, E.K., & Chuma, F.(2022). Approximation of misclassification probabilities in linear discriminant analysis based on repeated measurements. Journal of Communications in Statistics-Theory and Methods. https://doi.org/10.1080/03610926.2022.2062605

8. Ngailo, E.K., & Ngaruye, I.(2022). Asymptotic results for expected probability misclassifications in linear discriminant analysis with repeated measurements. Journal of Communications in Statistics-Theory and Methods. https://doi.org/10.1080/03610926.2022.2116286

9. Ngailo, E.K., von Rosen, D., & Singull, M. (2021). Asymptotic approximation of misclassification probabilities in linear discriminant analysis with repeated measurements. Acta et Commentationes Universitatis Tartuensis de Mathematica. https://doi.org/10.12697/acum.2021.2505

10. T. Bodnar, S. Mazur, Ngailo, E., & N. Parolya (2019). Discriminant analysis in small and large dimensions. Theory of Probability and Mathematical Statistics, 100: 24–42. https://doi.org/10.1090/tpms/1096