Vol. 2 No. 1 (2023): JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY YABATECH
Articles

BIASED EXPONENTIATED GAMMA DISTRIBUTIONS AND ITS VARIATES: STATISTICAL PROPERTIES AND ESTIMATION

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  • Ikegwu E. M.,
  • Amusa S. O.,
  • Kuranga Lola J.,
  • Ayodeji A.A.,
  • Alakija T. O.
Ikegwu E. M.
Department of Statistics, Yaba College of Technology, Yaba Lagos
Bio
Amusa S. O.
Department of Statistics, Yaba College of Technology, Yaba Lagos
Bio
Kuranga Lola J.
Department of Statistics, Institute of Applied Sciences, Kwara State Polytechnic, Ilorin
Bio
Ayodeji A.A.
Department of Statistics, Yaba College of Technology, Yaba Lagos
Bio
Alakija T. O.
Department of Statistics, Yaba College of Technology, Yaba Lagos
Bio

Published 2023-10-19

Keywords

  • Area-Biased, Exponentiated gamma distribution, Length-Biased, Markov Chain Monte Carlo, Skewness

How to Cite

Ikegwu E. M., Amusa S. O., Kuranga Lola J., Ayodeji A.A., & Alakija T. O. (2023). BIASED EXPONENTIATED GAMMA DISTRIBUTIONS AND ITS VARIATES: STATISTICAL PROPERTIES AND ESTIMATION. Journal of Science, Engineering and Technology YABATECH, 2(1). Retrieved from https://josetyabatech.com/index.php/home/article/view/17

Abstract

This study proposed the Exponentiated Gamma Distribution (EGD) derived from the

amalgamation of two existing distribution the exponential distribution and the gamma distribution

and explored its variates like the length - biased exponentiated gamma distribution (LeBEGD) and

area biased exponentiated gamma distribution (ABEGD). The mathematical expressions of these

distributions were also provided. The probability density functions, cumulative distribution

functions, hazard and survival functions of these distributions were generated and their statistical

properties like the mean, variance and other moments, skewness and kurtosis equally estimated,

tested and compared using the Markov Chain Monte Carlo (MCMC) simulated data. The result

from simulated data shows that the skewness and kurtosis reduce and tends to normality with

higher biases. The study concluded that weighting existing distributions makes them more flexible

and amenable to real-life situations.

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