Dr. John Kung’u

Dr. John Kung’u

Lectuer : School of pure,Applied & Health Sciences
jkungu@mut.ac.ke

Biography

Dr John Kung’u is a lecturer at the Department of Mathematics and Actuarial Science at Murang’a University of Technology, where he has been since March 2024. He is currently serving as the Examination and Time Tabling officer. Dr. Kung’u holds a PhD in Statistics from Kenyatta University. His research is focused in the field of statistics, with a special interest in survival analysis, sampling and dynamic models. In addition, he has made numerous contributions to the Statistics field as a research scientist. As a scholar, he has helped in the curriculum review of mathematical and statistical programs. He is also trained in monitoring and evaluation and strategic management and leadership. He has authored several publications in the field of statistics in peer-reviewed journals. He has also supervised students at the postgraduate level. Education/Professional Qualification: 2023 PhD in Statistics, Kenyatta University. 2011 MSc in Statistics, Kenyatta University. Research Areas Sampling, Survival analysis, Dynamic models, Multivariate analysis, Probability theory, Bayesian inference, Mathematical Modelling, Quality Control and Demography studies

Education

  • 2023: Ph.D. in Statistics- Kenyatta University
  • 2011: Master of Science in Statistics- Kenyatta University
  • 2006: Bachelor of Education in Science (Second upper-class Honors)- Moi University.

Publications

  1. Kung’u, , Odongo, L. and Kube, A. (2022) Classical Approach to Zero-Inflated Dynamic Panel Ordered Probit Model with an Application in Drug Abuse. American Journal of Theoretical and Applied Statistics. Vol. 11, No. 2, 58-74. http://dx.doi.org/10.11648/j.ajtas.20221102.11
  2. Kung’u, , Kube, A. and Odongo, L. (2022) Bayesian Approach to Zero-Inflated Dynamic Panel Ordered Probit Model with an Application in Drug Abuse. International Journal of Statistics and Applied Mathematics, 7(2): 01-13. http://dx.doi.org/10.22271/maths.2022.v7.i2a.787
  3. Chumba, G. and Kung’u, J. (2018) Modified Regression Type Estimators in the Presence of Non- Response in Two Phase International Journal of Scientific Research and Management (IJSRM). Vol. 06, M-2018-65-72. http://dx.doi.org/10.18535/ijsrm/v6i7.m01.
  4. Gitahi, J. N., Kung’u, J. and Odongo, L. (2017) Estimation of the Parameters of Poisson- Exponential Distribution Based on Progressively Type II Censoring Using the Expectation Maximization (EM) Algorithm. American Journal of Theoretical and Applied Statistics. 6, No. 3, pp. 141-149. http://dx.doi.org/10.11648/j.ajtas.20170603.12.
  5. Kung’u, J. and Nderitu, J. (2016) Generalized Ratio-Cum-Product Estimators for Two- Phase Sampling Using Multi-Auxiliary Variables. Open Journal of Statistics, 6, 616-627. http://dx.doi.org/10.4236/ojs.2016.64052.
  6. Tum, E. C., Kung’u, J. and Odongo, L. (2014) A New Regression Type Estimator with Two Auxiliary Variables for Single-Phase Open Journal of Statistics, 4, 789-796. http://dx.doi.org/10.4236/ojs.2014.49074.
  7. Waweru, P.M., Kung’u, J. and Kahiri, J. (2014) Mixture Ratio Estimators Using Multi- Auxiliary Variables and Attributes for Two-Phase Sampling. Open Journal of Statistics, 4, 776-788. http://dx.doi.org/10.4236/ojs.2014.49073.
  8. Kung’u, J., Chumba G. and Odongo, L. (2014) Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling. Open Journal of Statistics, 4, 355-366. http://dx.doi.org/10.4236/ojs.2014.45035.
  9. Mutembei, , Kung’u, J. and Ouma, C. (2014) Mixture Regression-Cum-Ratio Estimator Using Multi- Auxiliary Variables and Attributes in Single-Phase Sampling. Open Journal of Statistics, 4, 367-376. http://dx.doi.org/10.4236/ojs.2014.45036.
  10. Kung’u, J. and Odongo, L. (2014) Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Two-Phase Sampling. Open Journal of Statistics, 4, 246-257. http://dx.doi.org/10.4236/ojs.2014.44024.
  11. Kung’u, J. and Odongo, L. (2014) Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Single Phase Sampling. Open Journal of Statistics, 4, 239-245. http://dx.doi.org/10.4236/ojs.2014.4402.